CN110766736A - Defect detection method, defect detection device, electronic equipment and storage medium - Google Patents

Defect detection method, defect detection device, electronic equipment and storage medium Download PDF

Info

Publication number
CN110766736A
CN110766736A CN201911038208.1A CN201911038208A CN110766736A CN 110766736 A CN110766736 A CN 110766736A CN 201911038208 A CN201911038208 A CN 201911038208A CN 110766736 A CN110766736 A CN 110766736A
Authority
CN
China
Prior art keywords
region
target image
detected
defect
binarization
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911038208.1A
Other languages
Chinese (zh)
Other versions
CN110766736B (en
Inventor
刘小磊
王云奇
彭项君
赵晨曦
楚明磊
陈丽莉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BOE Technology Group Co Ltd
Beijing BOE Optoelectronics Technology Co Ltd
Original Assignee
BOE Technology Group Co Ltd
Beijing BOE Optoelectronics Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BOE Technology Group Co Ltd, Beijing BOE Optoelectronics Technology Co Ltd filed Critical BOE Technology Group Co Ltd
Priority to CN201911038208.1A priority Critical patent/CN110766736B/en
Publication of CN110766736A publication Critical patent/CN110766736A/en
Application granted granted Critical
Publication of CN110766736B publication Critical patent/CN110766736B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30148—Semiconductor; IC; Wafer

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Quality & Reliability (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

The invention discloses a defect detection method, a defect detection device, electronic equipment and a storage medium, wherein the defect detection method comprises the following steps: correcting the target image based on the background image; dividing the corrected target image into a plurality of local areas, and performing local enhancement processing on each local area; carrying out global enhancement processing on the target image subjected to the local enhancement processing, and carrying out binarization segmentation on the target image subjected to the global enhancement processing so as to extract the outline of a binarization region to be detected; and calculating the relative gray scale of the binaryzation to-be-detected area in the target image according to the outline of the binaryzation to-be-detected area, so as to detect the defect according to the relative gray scale. According to the invention, through local enhancement processing and global enhancement processing, large-area dark defects can be effectively extracted, so that defect detection can be accurately carried out.

Description

Defect detection method, defect detection device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a defect detection method and apparatus, an electronic device, and a storage medium.
Background
With the increasing complexity of semiconductor process flow, the requirements of each link in the middle of the production of the liquid crystal screen on the process are higher and higher, if a certain process flow does not meet the preset requirements, the defect similar to 'dirty' feeling can occur, and the defect is characterized in that: the area is large and is distributed in each place of the whole screen; the contrast is low, and the human eyes are difficult to accurately identify; the shapes are different and there is no clear shape. These screen defects have a great influence on the perception of human eyes, especially on the viewing experience of dynamic scenes. For process improvement, the first step is to detect all defects on the screen.
The existing defect detection method can only detect the defect with high contrast, but can not effectively and completely extract the outline of the dark defect with large area and low contrast, so that the detection result is inaccurate.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a defect detection method, apparatus, electronic device and storage medium, so as to solve the technical problems in the prior art.
According to a first aspect of the present invention, there is provided a defect detection method comprising:
correcting the target image based on the background image;
dividing the corrected target image into a plurality of local areas, and performing local enhancement processing on each local area;
carrying out global enhancement processing on the target image subjected to the local enhancement processing, and carrying out binarization segmentation on the target image subjected to the global enhancement processing so as to extract the outline of a binarization region to be detected;
and calculating the relative gray scale of the binaryzation to-be-detected area in the target image according to the outline of the binaryzation to-be-detected area, so as to detect the defect according to the relative gray scale.
In some embodiments of the present invention, the local enhancement processing is performed on each of the local regions, and includes:
enhancing each local image by histogram equalization;
and carrying out bilinear interpolation on the boundary of the local area.
In some embodiments of the present invention, before segmenting the rectified target image into a plurality of local regions, the method further includes:
performing de-texture processing on the corrected target image by using minimum filtering;
and smoothing the target image after the texture removal processing.
In some embodiments of the present invention, performing binarization segmentation on the target image after global enhancement processing to extract a contour of a binarized region to be measured, includes:
performing histogram calculation on the target image subjected to the global enhancement processing to obtain a two-dimensional gray level histogram;
determining a binarization threshold value according to a Rayleigh entropy maximum principle by using the two-dimensional gray level histogram;
and performing binarization segmentation on the target image subjected to global enhancement processing based on the binarization threshold value to obtain a binarization image, and extracting the outline of the binarization to-be-detected region from the binarization image.
In some embodiments of the present invention, calculating a relative gray scale of the binarized region-to-be-detected in the target image according to the contour of the binarized region-to-be-detected, so as to perform defect detection according to the relative gray scale, includes:
generating a circumscribed rectangle outside the binarization to-be-detected area according to the outline of the binarization to-be-detected area;
matching a detection area corresponding to the position of the external rectangle from the target image so as to match a to-be-detected area and a non-defect area in the detection area;
and calculating a relative gray value according to the gray value of the region to be detected in the detection region and the gray value of the non-defect region, so as to detect the defect according to the relative gray value.
In some embodiments of the present invention, calculating a relative gray-scale value according to the gray-scale value of the region to be detected in the detection region and the gray-scale value of the non-defect region includes:
calculating the average gray value of the region to be detected and the average gray value of the non-defect region according to the gray values of all pixel points of the region to be detected and the non-defect region in the detection region respectively;
and calculating a relative gray value according to the average gray value of the region to be detected and the average gray value of the non-defect region.
In some embodiments of the present invention, performing defect detection according to the relative gray-scale values comprises:
if the relative gray value is smaller than a first gray threshold value, judging the region to be detected in the detection region as a non-defect region;
and if the relative gray value is larger than a second gray threshold value, judging the region to be detected in the detection region as a defect.
In some embodiments of the invention, rectifying the target image based on the background image comprises:
for each pixel point in the target image, the following method is adopted for correction:
performing gray level normalization on the pixel points according to the gray values of the pixel points at the corresponding positions of the pixel points in the background image;
and stretching the pixel points after the gray level normalization.
According to a second aspect of the present invention, there is provided a defect detecting apparatus comprising:
a rectification module configured to rectify a target image based on a background image;
a local enhancement module configured to segment the rectified target image into a plurality of local regions, and perform local enhancement processing on each local region;
the global enhancement module is configured to perform global enhancement processing on the target image after the local enhancement processing, and perform binarization segmentation on the target image after the global enhancement processing so as to extract the outline of a binarization region to be detected;
and the detection module is configured to calculate the relative gray scale of the binarization area to be detected in the target image according to the outline of the binarization area to be detected, so as to detect defects according to the relative gray scale.
In some embodiments of the invention, the local boost module is further configured to:
enhancing each local image by histogram equalization;
and carrying out bilinear interpolation on the boundary of the local area.
In some embodiments of the invention, the local boost module is further configured to:
before the corrected target image is divided into a plurality of local areas, performing de-texture processing on the corrected target image by using minimum value filtering;
and smoothing the target image after the texture removal processing.
In some embodiments of the invention, the global augmentation module is further configured to:
performing histogram calculation on the target image subjected to the global enhancement processing to obtain a two-dimensional gray level histogram;
determining a binarization threshold value according to a Rayleigh entropy maximum principle by using the two-dimensional gray level histogram;
and performing binarization segmentation on the target image subjected to global enhancement processing based on the binarization threshold value to obtain a binarization image, and extracting the outline of the binarization to-be-detected region from the binarization image.
In some embodiments of the invention, the detection module is further configured to:
generating a circumscribed rectangle outside the binarization to-be-detected area according to the outline of the binarization to-be-detected area;
matching a detection area corresponding to the position of the external rectangle from the target image so as to match a to-be-detected area and a non-defect area in the detection area;
and calculating a relative gray value according to the gray value of the region to be detected in the detection region and the gray value of the non-defect region, so as to detect the defect according to the relative gray value.
In some embodiments of the invention, the detection module is further configured to:
calculating the average gray value of the region to be detected and the average gray value of the non-defect region according to the gray values of all pixel points of the region to be detected and the non-defect region in the detection region respectively;
and calculating a relative gray value according to the average gray value of the region to be detected and the average gray value of the non-defect region.
In some embodiments of the invention, the detection module is further configured to:
if the relative gray value is smaller than a first gray threshold value, judging the region to be detected in the detection region as a non-defect region;
and if the relative gray value is larger than a second gray threshold value, judging the region to be detected in the detection region as a defect.
In some embodiments of the invention, the orthotic module is further configured to: for each pixel point in the target image, the following method is adopted for correction:
performing gray level normalization on the pixel points according to the gray values of the pixel points at the corresponding positions of the pixel points in the background image;
and stretching the pixel points after the gray level normalization.
According to a third aspect of the present invention, there is provided an electronic device comprising a processor and a memory, the memory storing computer instructions, wherein the computer instructions, when executed by the processor, perform the defect detection method described in any of the above embodiments.
According to a fourth aspect of the present invention, there is provided a storage medium storing computer instructions adapted to be executed by a processor, the computer instructions, when executed by the processor, performing a defect detection method according to any of the above embodiments.
The defect detection method and the defect detection device provided by the embodiment of the invention can effectively extract the large-area dark defect through local enhancement processing and global enhancement processing, thereby accurately detecting the defect and overcoming the technical problems of low contrast of the dark defect and incapability of identifying human eyes. Therefore, the defect detection method provided by the embodiment of the invention can accurately detect the defects on the screen, is particularly suitable for detecting the dark defects with large area and low contrast, and provides reliable basis for subsequent process improvement and yield improvement.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a defect detection method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a circumscribed rectangle in an embodiment of the invention;
FIG. 3 is a flow chart of a defect detection method according to another embodiment of the present invention;
FIG. 4 is an image of a target image after stretching in an embodiment of the present invention;
FIG. 5 is an image after de-texturing and smoothing a target image according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating an image after local enhancement processing and bilinear interpolation are performed on a target image according to an embodiment of the present invention;
FIG. 7 is an image after global enhancement processing is performed on a target image in an embodiment of the present invention;
FIG. 8 is an image after binarization segmentation is performed on a target image according to an embodiment of the present invention;
FIG. 9 shows the result of defect detection on a target image according to an embodiment of the present invention;
FIG. 10 is a schematic structural diagram of a defect detection apparatus according to an embodiment of the present invention;
fig. 11 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In an embodiment of the present invention, as shown in fig. 1, an embodiment of the present invention provides a defect detection method, including:
step 101, rectifying a target image based on a background image.
Firstly, a screen to be detected is lightened, and the screen to be detected is shot by an industrial camera according to a certain sampling rate to obtain a target image. Then, the white paper or the white board is shot by an industrial camera to obtain a background image. Note that attention is paid to removing the influence of moire or the like on the detection result when capturing an image. The moire removing operation can be performed on the image after the image is shot so as to avoid the influence on the detection result. It should be noted that, in the embodiment of the present invention, both the target image and the background image are binarized images.
In step 101, for each pixel point in the target image, based on the corresponding pixel point in the background image, normalization and stretching are performed, thereby completing the correction of the target image.
Optionally, for each pixel point in the target image, the following method is adopted for correction:
performing gray level normalization on the pixel points according to the gray values of the pixel points at the corresponding positions of the pixel points in the background image; and stretching the pixel points after the gray level normalization.
Optionally, for each pixel point in the target image, performing gray level normalization by using the following formula:
Pixcorrection＝PixTarget/PixBackground
Wherein, PixCorrectionNormalized gray value, PixTargetFor the ith pixel point in the target image,PixbackgroundIs the ith pixel point in the background image.
Alternatively, multiplying the normalized gray scale value by 255 may stretch the target image by a total of 256 gray scale levels from 0 to 255.
Step 102, dividing the corrected target image into a plurality of local areas, and performing local enhancement processing on each local area.
Optionally, before step 102, further comprising: performing de-texture processing on the corrected target image by using minimum filtering; and smoothing the target image after the texture removal processing. Optionally, the rectified target image is subjected to a convolution de-texturing operation by using minimum value filtering (such as a convolution kernel of 3 × 3 or 5 × 5), so as to remove pixel textures of the target image, and simultaneously retain defects in the target image. After the texture removing operation, the target image is subjected to Gaussian filtering once, so that the image can be further smoothed.
Optionally, in step 102, the target image is segmented into a plurality of local regions according to the size W × H of the target image. For example, the target image is segmented into local regions of 5 × 5, 8 × 8, 10 × 10, or the like. Then, the enhancement processing is performed on each local region, so as to avoid the non-uniformity of the image being highlighted after the global enhancement processing is performed on the target image, and the defects cannot be accurately extracted by a segmentation algorithm performed later. Optionally, histogram equalization may be adopted to perform enhancement processing on each local image separately.
Because each local region is subjected to enhancement processing, so that (W/8) × (H/8) local regions are inevitably generated on an image, bilinear interpolation is required to be carried out on the boundary of each local region, and the phenomenon of image 'gridding' caused by the region segmentation processing is eliminated.
And 103, carrying out global enhancement processing on the target image subjected to the local enhancement processing, and carrying out binarization segmentation on the target image subjected to the global enhancement processing so as to extract the outline of the binarization area to be detected.
The target image after the local enhancement processing can be subjected to global enhancement processing by histogram equalization so as to highlight the defects in the target image, but the background in the target image is not affected.
Optionally, a maximum entropy threshold segmentation method is adopted to perform binarization segmentation on the target image after the global enhancement processing so as to extract the outline of the binarization to-be-detected region. Specifically, the method may include: performing histogram calculation on the target image subjected to the global enhancement processing to obtain a two-dimensional gray level histogram; determining a binarization threshold value according to a Rayleigh entropy maximum principle by using the two-dimensional gray level histogram; and performing binarization segmentation on the target image subjected to global enhancement processing based on the binarization threshold value to obtain a binarization image, and extracting the outline of the binarization to-be-detected region from the binarization image.
Optionally, before step 103, further comprising: and performing Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) on the target image after the local enhancement processing. Specifically, information in the image frequency domain is obtained, filtering is carried out by using a band-pass filter in the frequency domain, high-frequency noise of the image is eliminated, low-frequency information of the image is suppressed, and the processed image can be better subjected to defect extraction.
And 104, calculating the relative gray level of the binarization to-be-detected area in the target image according to the outline of the binarization to-be-detected area, thereby detecting the defect according to the relative gray level.
It should be noted that there may be a plurality of defects on the screen to be detected, so the outlines of a plurality of binarized regions to be detected can be extracted through step 103, and for each binarized region to be detected, defect detection is performed through step 104.
Optionally, step 104 comprises: generating a circumscribed rectangle outside the binarization to-be-detected area according to the outline of the binarization to-be-detected area; matching a detection area corresponding to the position of the external rectangle from the target image so as to match a to-be-detected area and a non-defect area in the detection area; and calculating a relative gray value according to the gray value of the region to be detected in the detection region and the gray value of the non-defect region, so as to detect the defect according to the relative gray value. In the embodiment of the invention, for each binarized region to be measured, a circumscribed rectangle is generated outside the binarized region to be measured, so that only one binarized region to be measured is in each circumscribed rectangle. Optionally, the length and the width of the binarized region to be measured may be respectively lengthened by 10% as the length and the width of the circumscribed rectangle, or may be respectively lengthened by 8% as the length and the width of the circumscribed rectangle, or may be respectively lengthened by 15% as the length and the width of the circumscribed rectangle, which is not limited in this embodiment of the present invention. After the circumscribed rectangle is generated, a non-defect area and an area to be detected in the circumscribed rectangle are found in the original target image. According to the embodiment of the invention, the non-defect area in the original target image is matched through the external rectangle, so that the interference of the defect in the external rectangle is eliminated, and the interference of other adjacent defects is eliminated.
Optionally, calculating a relative gray value according to the gray value of the region to be detected in the detection region and the gray value of the non-defect region, including: calculating the average gray value of the region to be detected and the average gray value of the non-defect region according to the gray values of all pixel points of the region to be detected and the non-defect region in the detection region respectively; and calculating a relative gray value according to the average gray value of the region to be detected and the average gray value of the non-defect region. As shown in FIG. 2, assume that the average gray-scale value of the region to be measured found in the original target image is C1The average gray value of the non-defect area found in the original target image is C2Then the following formula can be used to calculate the relative gray-scale value Cx：
Relative gray value Cx＝|C1-C2|/C2
Optionally, performing defect detection according to the relative gray value includes: if the relative gray value is smaller than a first gray threshold value, judging the region to be detected in the detection region as a non-defect region; and if the relative gray value is larger than a second gray threshold value, judging the region to be detected in the detection region as a defect. Can also preFirst, a first gray threshold X and a second gray threshold Y are set, and then, for each region to be measured extracted in step 103, if the relative gray value C of the region to be measured is the same as the gray value C of the region to be measuredxIf less than X, judging the region to be detected as non-defect, if the relative gray value C of the region to be detectedxIf the detected area is larger than Y, the area to be detected is judged to be a defect.
The defect detection method provided by the embodiment of the invention can effectively extract the large-area dark defect through local enhancement processing and global enhancement processing, thereby accurately detecting the defect and overcoming the technical problems that the contrast of the dark defect is low and the human eyes cannot distinguish. Therefore, the defect detection method provided by the embodiment of the invention can accurately detect the defects on the screen, is particularly suitable for detecting the dark defects with large area and low contrast, and provides reliable basis for subsequent process improvement and yield improvement.
As another embodiment of the present invention, as shown in fig. 3, the defect detecting method may include the steps of:
step 301, performing gray level normalization on the target image based on the background image.
And for each pixel point in the target image, carrying out gray level normalization on the pixel point according to the gray level value of the pixel point at the corresponding position of the pixel point in the background image. Optionally, the following formula is used for gray level normalization:
Pixcorrection＝PixTarget/PixBackground
Wherein, PixCorrectionNormalized gray value, PixTargetIs the ith pixel point, Pix, in the target imageBackgroundIs the ith pixel point in the background image.
And 302, stretching the target image after the gray level normalization.
The normalized gray value is multiplied by 255 to stretch the target image by 256 gray levels as shown in fig. 4.
And 303, performing de-texture processing on the corrected target image by using minimum filtering, and performing smoothing processing on the de-texture processed target image.
Optionally, the rectified target image is subjected to a convolution de-texturing operation by using minimum value filtering (such as a convolution kernel of 3 × 3 or 5 × 5), so as to remove pixel textures of the target image, and simultaneously retain defects in the target image. The de-texturing operation is followed by a gaussian filtering of the target image to further smooth the image, as shown in fig. 5.
And 304, dividing the smoothed target image into a plurality of local areas, respectively performing enhancement processing on each local area by histogram equalization, and performing bilinear interpolation on the boundary of each local area.
Firstly, dividing a target image into a plurality of local areas according to the size W x H of the target image; then, each local area is subjected to enhancement processing by histogram equalization, so that defects can be accurately extracted in the subsequent steps; finally, bilinear interpolation is performed on the boundary of each local area to eliminate the image 'grid' phenomenon, as shown in fig. 6.
And 305, performing fast Fourier transform and inverse fast Fourier transform on the target image after bilinear interpolation.
And step 306, performing global enhancement processing on the target image after the inverse fast Fourier transform.
Histogram equalization may be used to perform global enhancement processing on the target image after the ifft to highlight defects in the target image, as shown in fig. 7.
And 307, performing binarization segmentation on the target image subjected to the global enhancement by adopting a maximum entropy threshold segmentation method to extract the outline of the binarization to-be-detected region.
Specifically, the method may include: performing histogram calculation on the target image subjected to the global enhancement processing to obtain a two-dimensional gray level histogram; determining a binarization threshold value according to a Rayleigh entropy maximum principle by using the two-dimensional gray level histogram; and performing binarization segmentation on the target image subjected to the global enhancement processing based on the binarization threshold value to obtain a binarization image, as shown in fig. 8, and then extracting the outline of the binarization to-be-detected region from the binarization image.
And 308, generating a circumscribed rectangle outside the binarization to-be-detected area according to the outline of the binarization to-be-detected area.
For each binarization to-be-detected area, a circumscribed rectangle is generated outside the binarization to-be-detected area, so that only one binarization to-be-detected area exists in each circumscribed rectangle. Optionally, the length and the width of the binarized region to be measured are respectively lengthened by 10% to be used as the length and the width of the circumscribed rectangle.
Step 309, matching a detection area corresponding to the position of the circumscribed rectangle from the target image, thereby matching a region to be detected and a non-defect region in the detection area.
After the circumscribed rectangle is generated, a non-defect area and an area to be detected in the circumscribed rectangle are found in the original target image. According to the embodiment of the invention, the non-defect area in the original target image is matched through the external rectangle, so that the interference of the defect in the external rectangle is eliminated, and the interference of other adjacent defects is eliminated.
And 310, calculating a relative gray value according to the gray value of the region to be detected in the detection area and the gray value of the non-defect area, so as to detect the defect according to the relative gray value.
Calculating the average gray value of the region to be detected and the average gray value of the non-defect region according to the gray values of all pixel points of the region to be detected and the non-defect region in the detection region respectively; and calculating a relative gray value according to the average gray value of the region to be detected and the average gray value of the non-defect region. As shown in FIG. 2, assume that the average gray-scale value of the region to be measured found in the original target image is C1The average gray value of the non-defect area found in the original target image is C2Then the following formula can be used to calculate the relative gray-scale value Cx：
Relative gray value Cx＝|C1-C2|/C2
If the relative gray value is smaller than a first gray threshold value, judging the region to be detected in the detection region as a non-defect region; and if the relative gray value is larger than a second gray threshold value, judging the region to be detected in the detection region as a defect.
There may be a plurality of defects on the screen to be detected, so the outlines of a plurality of binarized regions to be detected can be extracted through step 307, and for each binarized region to be detected, the step 308-310 is adopted to perform defect detection, and the detection result is shown in fig. 9.
In one embodiment, as shown in fig. 10, a defect detection apparatus is provided, the defect detection apparatus 1000 comprising a rectification module 1001, a local enhancement module 1002, a global enhancement module 1003, and a detection module 1004. Wherein the rectification module 1001 is configured to rectify the target image based on the background image; the local enhancement module 1002 is configured to segment the rectified target image into a plurality of local regions, and perform local enhancement processing on each local region; the global enhancement module 1003 is configured to perform global enhancement processing on the target image after the local enhancement processing, and perform binarization segmentation on the target image after the global enhancement processing to extract a contour of a binarization region to be detected; the detection module 1004 is configured to calculate a relative gray scale of the binarized region-to-be-detected in the target image according to the contour of the binarized region-to-be-detected, so as to perform defect detection according to the relative gray scale.
In some embodiments of the invention, the local boost module 1002 is further configured to:
enhancing each local image by histogram equalization;
and carrying out bilinear interpolation on the boundary of the local area.
In some embodiments of the invention, the local boost module 1002 is further configured to:
before the corrected target image is divided into a plurality of local areas, performing de-texture processing on the corrected target image by using minimum value filtering;
and smoothing the target image after the texture removal processing.
In some embodiments of the invention, the global boost module 1003 is further configured to:
performing histogram calculation on the target image subjected to the global enhancement processing to obtain a two-dimensional gray level histogram;
determining a binarization threshold value according to a Rayleigh entropy maximum principle by using the two-dimensional gray level histogram;
and performing binarization segmentation on the target image subjected to global enhancement processing based on the binarization threshold value to obtain a binarization image, and extracting the outline of the binarization to-be-detected region from the binarization image.
In some embodiments of the invention, the detection module 1004 is further configured to:
generating a circumscribed rectangle outside the binarization to-be-detected area according to the outline of the binarization to-be-detected area;
matching a detection area corresponding to the position of the external rectangle from the target image so as to match a to-be-detected area and a non-defect area in the detection area;
and calculating a relative gray value according to the gray value of the region to be detected in the detection region and the gray value of the non-defect region, so as to detect the defect according to the relative gray value.
In some embodiments of the invention, the detection module 1004 is further configured to:
calculating the average gray value of the region to be detected and the average gray value of the non-defect region according to the gray values of all pixel points of the region to be detected and the non-defect region in the detection region respectively;
and calculating a relative gray value according to the average gray value of the region to be detected and the average gray value of the non-defect region.
In some embodiments of the invention, the detection module 1004 is further configured to:
if the relative gray value is smaller than a first gray threshold value, judging the region to be detected in the detection region as a non-defect region;
and if the relative gray value is larger than a second gray threshold value, judging the region to be detected in the detection region as a defect.
In some embodiments of the invention, the remediation module 1001 is further configured to: for each pixel point in the target image, the following method is adopted for correction:
performing gray level normalization on the pixel points according to the gray values of the pixel points at the corresponding positions of the pixel points in the background image;
and stretching the pixel points after the gray level normalization.
The defect detection device provided by the embodiment of the invention can effectively extract the large-area dark defect through local enhancement processing and global enhancement processing, thereby accurately detecting the defect and overcoming the technical problems that the contrast of the dark defect is low and the human eyes cannot distinguish. Therefore, the defect detection device provided by the embodiment of the invention can accurately detect the defects on the screen, is particularly suitable for detecting the dark defects with large area and low contrast, and provides reliable basis for subsequent process improvement and yield improvement.
It will be understood by those skilled in the art that the division of the modules and units in the defect detection apparatus is merely illustrative, and in other embodiments, the defect detection apparatus may be divided into different modules and units as required to perform all or part of the functions of the defect detection apparatus.
There is also provided, according to an embodiment of the present invention, an electronic device, as shown in fig. 11, comprising a processor 1101 and a memory 1102, the memory 1102 being configured to store computer program instructions, the computer program instructions being adapted to be loaded by the processor 1101 and to perform the method of: correcting the target image based on the background image; dividing the corrected target image into a plurality of local areas, and performing local enhancement processing on each local area; carrying out global enhancement processing on the target image subjected to the local enhancement processing, and carrying out binarization segmentation on the target image subjected to the global enhancement processing so as to extract the outline of a binarization region to be detected; and calculating the relative gray scale of the binaryzation to-be-detected area in the target image according to the outline of the binaryzation to-be-detected area, so as to detect the defect according to the relative gray scale.
The processor may be any suitable processor, for example, implemented in the form of a central processing unit, a microprocessor, an embedded processor, or the like, and may employ an architecture such as X86, ARM, or the like; the memory 1102 may be any of a variety of suitable memory devices including, but not limited to, magnetic memory devices, semiconductor memory devices, optical memory devices, and the like, which are not limited by the embodiments of the present invention.
Any reference to memory, storage, database, or other medium used by the invention may include non-volatile and/or volatile memory. Suitable non-volatile Memory can include Read-Only Memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash Memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and bus dynamic RAM (RDRAM).
Further, according to an embodiment of the present invention, the processor 1101 may further load and execute: enhancing each local image by histogram equalization; and carrying out bilinear interpolation on the boundary of the local area.
Further, according to an embodiment of the present invention, the processor 1101 may further load and execute: before the corrected target image is divided into a plurality of local areas, performing de-texture processing on the corrected target image by using minimum value filtering; and smoothing the target image after the texture removal processing.
Further, according to an embodiment of the present invention, the processor 1101 may further load and execute: performing histogram calculation on the target image subjected to the global enhancement processing to obtain a two-dimensional gray level histogram; determining a binarization threshold value according to a Rayleigh entropy maximum principle by using the two-dimensional gray level histogram; and performing binarization segmentation on the target image subjected to global enhancement processing based on the binarization threshold value to obtain a binarization image, and extracting the outline of the binarization to-be-detected region from the binarization image.
Further, according to an embodiment of the present invention, the processor 1101 may further load and execute: generating a circumscribed rectangle outside the binarization to-be-detected area according to the outline of the binarization to-be-detected area; matching a detection area corresponding to the position of the external rectangle from the target image so as to match a to-be-detected area and a non-defect area in the detection area; and calculating a relative gray value according to the gray value of the region to be detected in the detection region and the gray value of the non-defect region, so as to detect the defect according to the relative gray value.
Further, according to an embodiment of the present invention, the processor 1101 may further load and execute: calculating the average gray value of the region to be detected and the average gray value of the non-defect region according to the gray values of all pixel points of the region to be detected and the non-defect region in the detection region respectively; and calculating a relative gray value according to the average gray value of the region to be detected and the average gray value of the non-defect region.
Further, according to an embodiment of the present invention, the processor 1101 may further load and execute: if the relative gray value is smaller than a first gray threshold value, judging the region to be detected in the detection region as a non-defect region; and if the relative gray value is larger than a second gray threshold value, judging the region to be detected in the detection region as a defect.
Further, according to an embodiment of the present invention, the processor 1101 may further load and execute: for each pixel point in the target image, the following method is adopted for correction:
performing gray level normalization on the pixel points according to the gray values of the pixel points at the corresponding positions of the pixel points in the background image; and stretching the pixel points after the gray level normalization.
Therefore, the embodiment of the invention can effectively extract the large-area dark defect through the local enhancement processing and the global enhancement processing, thereby accurately detecting the defect and overcoming the technical problems that the contrast of the dark defect is low and the human eyes cannot distinguish. Therefore, the defect detection method provided by the embodiment of the invention can accurately detect the defects on the screen, is particularly suitable for detecting the dark defects with large area and low contrast, and provides reliable basis for subsequent process improvement and yield improvement.
It should be noted that, for the sake of simplicity, the above-mentioned embodiments of the system, method and electronic device are all described as a series of acts or a combination of modules, but those skilled in the art should understand that the present invention is not limited by the described order of acts or the connection of modules, because some steps may be performed in other orders or simultaneously and some modules may be connected in other manners according to the present invention.
It should also be understood by those skilled in the art that the embodiments described in the specification are included in one embodiment, the number of the above embodiments is merely for description, and the actions and modules involved are not necessarily essential to the invention.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technical contents can be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. The storage medium includes volatile storage medium or non-volatile storage medium, such as various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (11)

1. A method of defect detection, comprising:
correcting the target image based on the background image;
dividing the corrected target image into a plurality of local areas, and performing local enhancement processing on each local area;
carrying out global enhancement processing on the target image subjected to the local enhancement processing, and carrying out binarization segmentation on the target image subjected to the global enhancement processing so as to extract the outline of a binarization region to be detected;
and calculating the relative gray scale of the binaryzation to-be-detected area in the target image according to the outline of the binaryzation to-be-detected area, so as to detect the defect according to the relative gray scale.
2. The defect detection method of claim 1, wherein performing the local enhancement processing on each of the local regions comprises:
enhancing each local image by histogram equalization;
and carrying out bilinear interpolation on the boundary of the local area.
3. The defect detection method according to claim 1, further comprising, before dividing the corrected target image into a plurality of local regions:
performing de-texture processing on the corrected target image by using minimum filtering;
and smoothing the target image after the texture removal processing.
4. The defect detection method according to claim 1, wherein performing binarization segmentation on the target image after global enhancement processing to extract a contour of a binarized region to be detected comprises:
performing histogram calculation on the target image subjected to the global enhancement processing to obtain a two-dimensional gray level histogram;
determining a binarization threshold value according to a Rayleigh entropy maximum principle by using the two-dimensional gray level histogram;
and performing binarization segmentation on the target image subjected to global enhancement processing based on the binarization threshold value to obtain a binarization image, and extracting the outline of the binarization to-be-detected region from the binarization image.
5. The defect detection method according to claim 1, wherein calculating a relative gray level of the binarized region-to-be-detected in the target image based on the contour of the binarized region-to-be-detected, thereby performing defect detection based on the relative gray level, comprises:
generating a circumscribed rectangle outside the binarization to-be-detected area according to the outline of the binarization to-be-detected area;
matching a detection area corresponding to the position of the external rectangle from the target image so as to match a to-be-detected area and a non-defect area in the detection area;
and calculating a relative gray value according to the gray value of the region to be detected in the detection region and the gray value of the non-defect region, so as to detect the defect according to the relative gray value.
6. The defect detection method of claim 5, wherein calculating a relative gray value according to the gray value of the region to be detected in the detection area and the gray value of the non-defect area comprises:
calculating the average gray value of the region to be detected and the average gray value of the non-defect region according to the gray values of all pixel points of the region to be detected and the non-defect region in the detection region respectively;
and calculating a relative gray value according to the average gray value of the region to be detected and the average gray value of the non-defect region.
7. The defect detection method of claim 5, wherein performing defect detection based on the relative gray scale values comprises:
if the relative gray value is smaller than a first gray threshold value, judging the region to be detected in the detection region as a non-defect region;
and if the relative gray value is larger than a second gray threshold value, judging the region to be detected in the detection region as a defect.
8. The defect detection method of claim 1, wherein the rectifying the target image based on the background image comprises:
for each pixel point in the target image, the following method is adopted for correction:
performing gray level normalization on the pixel points according to the gray values of the pixel points at the corresponding positions of the pixel points in the background image;
and stretching the pixel points after the gray level normalization.
9. A defect detection apparatus, comprising:
a rectification module configured to rectify a target image based on a background image;
a local enhancement module configured to segment the rectified target image into a plurality of local regions, and perform local enhancement processing on each local region;
the global enhancement module is configured to perform global enhancement processing on the target image after the local enhancement processing, and perform binarization segmentation on the target image after the global enhancement processing so as to extract the outline of a binarization region to be detected;
and the detection module is configured to calculate the relative gray scale of the binarization area to be detected in the target image according to the outline of the binarization area to be detected, so as to detect defects according to the relative gray scale.
10. An electronic device comprising a processor and a memory, the memory storing computer instructions, wherein the computer instructions, when executed by the processor, perform the defect detection method of any one of claims 1-8.
11. A storage medium storing computer instructions adapted to be executed by a processor, the computer instructions, when executed by the processor, performing a defect detection method according to any one of claims 1-8.
CN201911038208.1A 2019-10-29 2019-10-29 Defect detection method, defect detection device, electronic equipment and storage medium Active CN110766736B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911038208.1A CN110766736B (en) 2019-10-29 2019-10-29 Defect detection method, defect detection device, electronic equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911038208.1A CN110766736B (en) 2019-10-29 2019-10-29 Defect detection method, defect detection device, electronic equipment and storage medium

Publications (2)

Publication Number Publication Date
CN110766736A true CN110766736A (en) 2020-02-07
CN110766736B CN110766736B (en) 2022-10-14

Family

ID=69334331

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911038208.1A Active CN110766736B (en) 2019-10-29 2019-10-29 Defect detection method, defect detection device, electronic equipment and storage medium

Country Status (1)

Country Link
CN (1) CN110766736B (en)

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111524102A (en) * 2020-04-10 2020-08-11 京东方科技集团股份有限公司 Method and device for detecting screen contamination of liquid crystal display
CN111583216A (en) * 2020-04-30 2020-08-25 深圳比特微电子科技有限公司 Defect detection method for PCBA
CN111815600A (en) * 2020-07-04 2020-10-23 博科视(苏州)技术有限公司 Visual sense-based annular magnetic steel appearance defect detection method
CN111855672A (en) * 2020-07-29 2020-10-30 佛山市南海区广工大数控装备协同创新研究院 Method for detecting COF flexible board defects
CN112053356A (en) * 2020-09-17 2020-12-08 惠州高视科技有限公司 Method and device for detecting defects at pins of plastic packaged semiconductor
CN112348773A (en) * 2020-09-28 2021-02-09 歌尔股份有限公司 Screen defect detection method and device and electronic equipment
CN112381745A (en) * 2020-11-06 2021-02-19 山东大学 Side-scan sonar image data enhancement method and system based on multi-source data synthesis
CN112418198A (en) * 2021-01-25 2021-02-26 城云科技（中国）有限公司 Method for detecting fluctuation defects of floor tiles of pedestrian walkways based on gray scale map energy values
CN112435248A (en) * 2020-11-30 2021-03-02 深圳市泰沃德技术有限公司 Defect detection method, defect detection device, control device and readable storage medium
CN113436131A (en) * 2020-03-04 2021-09-24 上海微创卜算子医疗科技有限公司 Defect detection method, defect detection device, electronic equipment and storage medium
CN113538430A (en) * 2021-09-16 2021-10-22 深圳新视智科技术有限公司 Pole piece defect detection method, device, equipment and medium based on difference
CN113781406A (en) * 2021-08-20 2021-12-10 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Scratch detection method and device for electronic component and computer equipment
CN114152627A (en) * 2022-02-09 2022-03-08 季华实验室 Chip circuit defect detection method and device, electronic equipment and storage medium
CN114298985A (en) * 2021-12-16 2022-04-08 苏州凌云视界智能设备有限责任公司 Defect detection method, device, equipment and storage medium
CN114764804A (en) * 2022-06-16 2022-07-19 深圳新视智科技术有限公司 Lithium battery pole piece defect detection method, device, equipment and storage medium
CN114972367A (en) * 2021-02-25 2022-08-30 上海复旦微电子集团股份有限公司 Method, device, equipment and computer readable storage medium for segmenting image
CN116416181A (en) * 2021-12-29 2023-07-11 广州镭晨智能装备科技有限公司 Pin defect detection method, detection equipment and storage medium
CN117197006A (en) * 2023-09-27 2023-12-08 上海世禹精密设备股份有限公司 Region filtering method, region filtering device, electronic equipment and computer readable storage medium
CN117252876A (en) * 2023-11-17 2023-12-19 江西斯迈得半导体有限公司 LED support defect detection method and system

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101859432A (en) * 2010-05-17 2010-10-13 重庆师范大学 Construction method of document image enhancement
CN102722871A (en) * 2012-05-24 2012-10-10 中山大学 Quick and effective image enhancing method
CN103745433A (en) * 2013-12-05 2014-04-23 莱阳市科盾通信设备有限责任公司 Vehicle safety auxiliary video image processing method
CN104408400A (en) * 2014-10-28 2015-03-11 北京理工大学 Indistinguishable multi-target detection method based on single-image frequency domain information
CN104851086A (en) * 2015-04-17 2015-08-19 武汉恒兴通检测有限公司 Image detection method for cable rope surface defect
CN105608676A (en) * 2015-12-23 2016-05-25 浙江宇视科技有限公司 Video image enhancement method and device
US20170270664A1 (en) * 2016-03-21 2017-09-21 The Board Of Trustees Of The Leland Stanford Junior University Methods for characterizing features of interest in digital images and systems for practicing same
CN109727233A (en) * 2018-12-18 2019-05-07 武汉精立电子技术有限公司 A kind of LCD defect inspection method
CN109784344A (en) * 2019-01-24 2019-05-21 中南大学 A kind of non-targeted filtering method of image for ground level mark identification

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101859432A (en) * 2010-05-17 2010-10-13 重庆师范大学 Construction method of document image enhancement
CN102722871A (en) * 2012-05-24 2012-10-10 中山大学 Quick and effective image enhancing method
CN103745433A (en) * 2013-12-05 2014-04-23 莱阳市科盾通信设备有限责任公司 Vehicle safety auxiliary video image processing method
CN104408400A (en) * 2014-10-28 2015-03-11 北京理工大学 Indistinguishable multi-target detection method based on single-image frequency domain information
CN104851086A (en) * 2015-04-17 2015-08-19 武汉恒兴通检测有限公司 Image detection method for cable rope surface defect
CN105608676A (en) * 2015-12-23 2016-05-25 浙江宇视科技有限公司 Video image enhancement method and device
US20170270664A1 (en) * 2016-03-21 2017-09-21 The Board Of Trustees Of The Leland Stanford Junior University Methods for characterizing features of interest in digital images and systems for practicing same
CN109727233A (en) * 2018-12-18 2019-05-07 武汉精立电子技术有限公司 A kind of LCD defect inspection method
CN109784344A (en) * 2019-01-24 2019-05-21 中南大学 A kind of non-targeted filtering method of image for ground level mark identification

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
赵川源: ""基于图像和光谱技术的果实识别与病害检测方法研究"", 《中国博士学位论文全文数据库 农业科技辑》 *

Cited By (32)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113436131A (en) * 2020-03-04 2021-09-24 上海微创卜算子医疗科技有限公司 Defect detection method, defect detection device, electronic equipment and storage medium
CN111524102A (en) * 2020-04-10 2020-08-11 京东方科技集团股份有限公司 Method and device for detecting screen contamination of liquid crystal display
CN111524102B (en) * 2020-04-10 2024-04-16 京东方科技集团股份有限公司 Screen dirt detection method and device of liquid crystal display
CN111583216A (en) * 2020-04-30 2020-08-25 深圳比特微电子科技有限公司 Defect detection method for PCBA
CN111815600A (en) * 2020-07-04 2020-10-23 博科视(苏州)技术有限公司 Visual sense-based annular magnetic steel appearance defect detection method
CN111815600B (en) * 2020-07-04 2024-04-02 博科视(苏州)技术有限公司 Visual-based annular magnetic steel appearance defect detection method
CN111855672A (en) * 2020-07-29 2020-10-30 佛山市南海区广工大数控装备协同创新研究院 Method for detecting COF flexible board defects
CN112053356B (en) * 2020-09-17 2021-08-20 高视科技(苏州)有限公司 Method and device for detecting defects at pins of plastic packaged semiconductor
CN112053356A (en) * 2020-09-17 2020-12-08 惠州高视科技有限公司 Method and device for detecting defects at pins of plastic packaged semiconductor
CN112348773A (en) * 2020-09-28 2021-02-09 歌尔股份有限公司 Screen defect detection method and device and electronic equipment
CN112348773B (en) * 2020-09-28 2022-09-23 歌尔股份有限公司 Screen defect detection method and device and electronic equipment
CN112381745B (en) * 2020-11-06 2023-09-22 山东大学 Side-scan sonar image data enhancement method and system based on multi-source data synthesis
CN112381745A (en) * 2020-11-06 2021-02-19 山东大学 Side-scan sonar image data enhancement method and system based on multi-source data synthesis
CN112435248A (en) * 2020-11-30 2021-03-02 深圳市泰沃德技术有限公司 Defect detection method, defect detection device, control device and readable storage medium
CN112418198B (en) * 2021-01-25 2021-04-13 城云科技（中国）有限公司 Method for detecting fluctuation defects of floor tiles of pedestrian walkways based on gray scale map energy values
CN112418198A (en) * 2021-01-25 2021-02-26 城云科技（中国）有限公司 Method for detecting fluctuation defects of floor tiles of pedestrian walkways based on gray scale map energy values
CN114972367B (en) * 2021-02-25 2024-06-07 上海复旦微电子集团股份有限公司 Method, apparatus, device and computer readable storage medium for segmenting images
CN114972367A (en) * 2021-02-25 2022-08-30 上海复旦微电子集团股份有限公司 Method, device, equipment and computer readable storage medium for segmenting image
CN113781406B (en) * 2021-08-20 2024-04-02 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Scratch detection method and device for electronic component and computer equipment
CN113781406A (en) * 2021-08-20 2021-12-10 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） Scratch detection method and device for electronic component and computer equipment
CN113538430A (en) * 2021-09-16 2021-10-22 深圳新视智科技术有限公司 Pole piece defect detection method, device, equipment and medium based on difference
CN114298985B (en) * 2021-12-16 2023-12-22 苏州凌云光工业智能技术有限公司 Defect detection method, device, equipment and storage medium
CN114298985A (en) * 2021-12-16 2022-04-08 苏州凌云视界智能设备有限责任公司 Defect detection method, device, equipment and storage medium
CN116416181A (en) * 2021-12-29 2023-07-11 广州镭晨智能装备科技有限公司 Pin defect detection method, detection equipment and storage medium
CN116416181B (en) * 2021-12-29 2024-05-31 广州镭晨智能装备科技有限公司 Pin defect detection method, detection equipment and storage medium
CN114152627A (en) * 2022-02-09 2022-03-08 季华实验室 Chip circuit defect detection method and device, electronic equipment and storage medium
CN114764804B (en) * 2022-06-16 2022-09-20 深圳新视智科技术有限公司 Lithium battery pole piece defect detection method, device, equipment and storage medium
CN114764804A (en) * 2022-06-16 2022-07-19 深圳新视智科技术有限公司 Lithium battery pole piece defect detection method, device, equipment and storage medium
CN117197006A (en) * 2023-09-27 2023-12-08 上海世禹精密设备股份有限公司 Region filtering method, region filtering device, electronic equipment and computer readable storage medium
CN117197006B (en) * 2023-09-27 2024-05-10 上海世禹精密设备股份有限公司 Region filtering method, region filtering device, electronic equipment and computer readable storage medium
CN117252876A (en) * 2023-11-17 2023-12-19 江西斯迈得半导体有限公司 LED support defect detection method and system
CN117252876B (en) * 2023-11-17 2024-02-09 江西斯迈得半导体有限公司 LED support defect detection method and system

Also Published As

Publication number Publication date
CN110766736B (en) 2022-10-14

Similar Documents

Publication Publication Date Title
CN110766736B (en) Defect detection method, defect detection device, electronic equipment and storage medium
Ntirogiannis et al. A combined approach for the binarization of handwritten document images
CN110414507B (en) License plate recognition method and device, computer equipment and storage medium
JP6507846B2 (en) Image noise removing method and image noise removing apparatus
EP2819091B1 (en) Method and apparatus for processing a gray image
CN110163219B (en) Target detection method based on image edge recognition
Jung et al. Robust watershed segmentation using wavelets
JP5534411B2 (en) Image processing device
Deshpande et al. A novel modified cepstral based technique for blind estimation of motion blur
CN113781406B (en) Scratch detection method and device for electronic component and computer equipment
Chen et al. Directional high-pass filter for blurry image analysis
CN113723309A (en) Identity recognition method, identity recognition device, equipment and storage medium
CN104732510A (en) Camera lens black spot detecting method and device
CN112801031A (en) Vein image recognition method and device, electronic equipment and readable storage medium
CN111476750A (en) Method, device and system for carrying out stain detection on imaging module and storage medium
CN108960247B (en) Image significance detection method and device and electronic equipment
Bella et al. Reduction of the non-uniform illumination using nonlocal variational models for document image analysis
CN117496560B (en) Fingerprint line identification method and device based on multidimensional vector
WO2017128646A1 (en) Image processing method and device
CN112419228B (en) Method and device for detecting three-dimensional edge defect of cover plate
CN113744294A (en) Image processing method and related device
CN109211919B (en) Method and device for identifying magnetic tile defect area
CN111179245A (en) Image quality detection method, device, electronic equipment and storage medium
Yasmin et al. Performance Study of Soft Local Binary Pattern over Local Binary Pattern under Noisy Images
CN112329572B (en) Rapid static living body detection method and device based on frame and flash point

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant