CN112215827A - Electromigration region detection method and device, computer equipment and storage medium - Google Patents
Electromigration region detection method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to an electromigration region detection method, an electromigration region detection device, a computer device and a storage medium. The method comprises the following steps: acquiring a circuit board image; performing edge detection on the circuit board image to obtain edge information of the circuit board image; identifying interferents in the circuit board image based on the edge information; removing the interference object from the circuit board image to obtain a target circuit board image without the interference object; and extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm. By adopting the method, the efficiency of detecting the electromigration region can be improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for detecting an electromigration region, a computer device, and a storage medium.
Background
Electromigration, which is a typical failure mode of electronic products, is a phenomenon in which metal atoms/ions are driven by the electric wind force to migrate along the direction of movement of electrons under the action of high current density. Valleys are formed at the negative electrode due to migration of the substance, and hillocks are formed at the positive electrode due to accumulation of the substance. The formation of valleys and hillocks induces an open or short circuit of the circuit, resulting in device failure.
At present, the electromigration region on the device is identified mainly by depending on manual visual inspection by an inspector, so that error detection or omission occurs easily, and multiple inspections are needed, so that the electromigration region detection efficiency is low.
Disclosure of Invention
In view of the foregoing, there is a need to provide a method, an apparatus, a computer device and a storage medium for detecting an electromigration region, which address the technical problem of inefficient detection of an electromigration region.
A method of electromigration region detection, the method comprising:
acquiring a circuit board image;
performing edge detection on the circuit board image to obtain edge information of the circuit board image;
identifying interferents in the circuit board image based on the edge information;
removing the interference object from the circuit board image to obtain a target circuit board image without the interference object;
and extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm.
In one embodiment, the method further comprises:
acquiring a preset number of sample circuit board images;
marking the sample circuit board image to obtain a training sample;
inputting the training sample into a target detection network for training to obtain an interferent recognition model;
the identifying the interferent in the circuit board image based on the edge information includes:
and identifying the interferent in the circuit board image based on the edge information through the interferent identification model.
In one embodiment, before performing the edge detection on the circuit board image, the method further includes:
and carrying out preprocessing operation on the circuit board image, wherein the preprocessing operation comprises image graying, morphological processing and median filtering processing.
In one embodiment, the extracting the electromigration region in the target circuit board image by a visual saliency detection algorithm includes:
converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space;
and extracting an electromigration region in the circuit board image without the interference object in the preset color space through a visual saliency detection algorithm.
In one embodiment, after the extracting, by the visual saliency detection algorithm, the electromigration region in the target circuit board image, the method further comprises:
calculating the area of an electromigration region in the circuit board image;
determining the short circuit risk level of the circuit board corresponding to the circuit board image according to the area;
and generating risk prompt information corresponding to the short circuit risk level and prompting.
An electromigration region detection apparatus, the apparatus comprising:
the image acquisition module is used for acquiring a circuit board image;
the edge detection module is used for carrying out edge detection on the circuit board image to obtain edge information of the circuit board image;
the image identification module is used for identifying interferents in the circuit board image based on the edge information;
the interference object removing module is used for removing the interference object from the circuit board image to obtain a target circuit board image without the interference object;
and the electromigration region extraction module is used for extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm.
In one embodiment, the apparatus further comprises:
the sample circuit board image acquisition module is used for acquiring a preset number of sample circuit board images;
the marking module is used for marking the sample circuit board image to obtain a training sample;
the model training module is used for inputting the training sample into a target detection network for training to obtain an interferent recognition model;
the image recognition module is further configured to:
and identifying the interferent in the circuit board image based on the edge information through the interferent identification model.
In one embodiment, the apparatus further comprises:
and the preprocessing module is used for preprocessing the circuit board image, and the preprocessing operation comprises image graying, morphological processing and median filtering processing.
In one embodiment, the electromigration region extraction module is further configured to:
converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space;
and extracting an electromigration region in the circuit board image without the interference object in the preset color space through a visual saliency detection algorithm.
In one embodiment, the apparatus further comprises:
the area calculation module is used for calculating the area of the electromigration region in the circuit board image;
the short circuit risk grade determining module is used for determining the short circuit risk grade of the circuit board corresponding to the circuit board image according to the area;
and the prompt module is used for generating risk prompt information corresponding to the short circuit risk level and prompting.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
After the circuit board image is obtained, the electromigration region detection method, the electromigration region detection device, the computer equipment and the storage medium carry out edge detection on the circuit board image to obtain edge information of the circuit board image; identifying interferents in the circuit board image based on the edge information; removing the interferent from the circuit board image to obtain a target circuit board image without the interferent; the electromigration region in the target circuit board image is extracted through a visual saliency detection algorithm, so that the electromigration region on a circuit board device can be detected without manual work, and the efficiency and the accuracy of electromigration region detection are improved.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of an electromigration region detection method;
FIG. 2 is a flowchart illustrating a method for electromigration region detection in one embodiment;
FIG. 3 is an original circuit board image in one embodiment;
FIG. 4 is an image of a circuit board to be inspected in one embodiment;
FIG. 5 is an image of a test result in one embodiment;
FIG. 6 is a flowchart illustrating a method for electromigration region detection in another embodiment;
FIG. 7 is a block diagram of an electromigration region detection apparatus in one embodiment;
FIG. 8 is a block diagram showing the structure of an electromigration region detection apparatus in another embodiment;
FIG. 9 is a diagram showing an internal structure of a computer device in one embodiment;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The electromigration region detection method provided by the present application may be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The electromigration region detection method can be applied to a terminal or a server, and can also be realized through the interaction of the terminal and the server, for example, when the method is applied to the terminal, the terminal 102 acquires a circuit board image; performing edge detection on the circuit board image to obtain edge information of the circuit board image; identifying interferents in the circuit board image based on the edge information; removing the interferent from the circuit board image to obtain a target circuit board image without the interferent; and extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, there is provided an electromigration region detection method, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
s202, obtaining a circuit board image.
The circuit board image is an image obtained by image acquisition of electronic elements such as a circuit board, a chip and the like to be detected through image acquisition equipment.
In one embodiment, an image acquisition device acquires an image of an electronic component such as a circuit board, a chip and the like to be detected to obtain an original circuit board image, and sends the acquired original circuit board image to a terminal, so that the terminal performs electromigration region detection on the original circuit board image. Wherein the image capturing device may be a high resolution camera.
In one embodiment, after acquiring an original circuit board image uploaded by an image acquisition device, a terminal may directly use the original circuit board image as a circuit board image to be detected, or may extract an area to be detected from the original circuit board image, and use the extracted area to be detected as the circuit board image to be detected. The terminal can perform image segmentation on the original circuit board image by using a GrabCut algorithm, and when the image is segmented, the region to be detected is used as a foreground, and the region outside the region to be detected in the original circuit board image is used as a background, so that the region to be detected is extracted from the original circuit board image.
For example, fig. 3 shows an original circuit board image, when the image is segmented, an area inside a rectangular frame (an area to be detected) is used as a foreground, an area outside the rectangular frame is used as a background, so that the area to be detected is extracted from the circuit board image, and the extraction result is shown in fig. 4.
And S204, carrying out edge detection on the circuit board image to obtain edge information of the circuit board image.
The edge detection is the measurement, detection and positioning of the gray scale change of the circuit board image. Edge information is an important feature of an image, and an edge refers to a set of pixels with a step change or a roof change in the gray level of surrounding pixels. The edge detection algorithm comprises a differential operator, a Laplace Gaussian operator, a canny operator and the like.
In one embodiment, after the terminal acquires the circuit board image (i.e., the circuit board image to be detected), the edge detection algorithm is used to perform edge detection on the circuit board image, so as to obtain edge information of the circuit board image. Specifically, the terminal adopts a canny operator to carry out edge detection on the circuit board image, wherein the canny operator aims at finding an optimal edge detection algorithm, and the meaning of the optimal edge detection is as follows: good detection-algorithms are able to identify as many actual edges in the image as possible, good localization-identified edges are as close as possible to actual edges in the actual image, minimal response-edges in the image can only be identified once and possible image noise should not be identified as edges.
S206, identifying the interferent in the circuit board image based on the edge information.
The interferent is an interference area affecting determination of the electromigration area, and may be specifically an interference object affecting determination of the electromigration area, such as characters in an image of the circuit board. For example, the character "103" in fig. 4 is the interfering object to be identified.
In one embodiment, after the terminal obtains the edge information of the circuit board image, the edge information is used as the image feature of the circuit board image, and a pre-trained interferent recognition model is adopted to recognize interferents in the circuit board image based on the image feature. The interferent recognition model may be obtained by pre-training based on the YOLOv3 network. Wherein YOLOv3 is a regression model based target detection algorithm.
And S208, removing the interference object from the circuit board image to obtain a target circuit board image without the interference object.
In one embodiment, the terminal removes identified distractors from the circuit board image after identifying the distractors in the circuit board image, wherein the terminal may employ the GrabCut algorithm to remove the identified distractors from the circuit board image.
Specifically, the GrabCut algorithm is adopted to carry out image segmentation on the circuit board image, and during image segmentation, the identified interferent is used as a background, and the region except the interferent in the circuit board image is used as a foreground, so that the interferent is removed from the circuit board image, and the target circuit board image without the interferent is obtained.
S210, extracting an electromigration region in the target circuit board image through a visual saliency detection algorithm.
Wherein, the visual saliency detection algorithm is used for extracting a salient region (namely a region of interest of a human) in an image by simulating the visual characteristics of the human. The electromigration region in this application is the significant region.
Specifically, the terminal calculates a color mean value of the target circuit board image, calculates a two-norm between a color value and the color mean value of each pixel point in the target circuit board image, generates a saliency map based on the two-norm corresponding to each pixel point in the target circuit board image, and extracts an electromigration region from the saliency map. Generating a significant image based on the two norms corresponding to each pixel point in the target circuit board image comprises the following steps: and determining the two norms corresponding to the pixel points in the target circuit board image as the color values of the significant image to be generated.
In the embodiment, after the terminal acquires the circuit board image, the edge of the circuit board image is detected to obtain the edge information of the circuit board image; identifying interferents in the circuit board image based on the edge information; removing the interferent from the circuit board image to obtain a target circuit board image without the interferent; the electromigration region in the target circuit board image is extracted through a visual saliency detection algorithm, so that the electromigration region on a circuit board device can be detected without manual work, and the efficiency and the accuracy of electromigration region detection are improved.
In one embodiment, the electromigration region detection method further includes the following steps: acquiring a preset number of sample circuit board images; marking the sample circuit board image to obtain a training sample; and inputting the training sample into a target detection network for training to obtain an interferent recognition model. Wherein the target detection network may be a YOLOv3 network.
Specifically, the terminal may acquire a sample circuit board image through a high-resolution camera, or download the sample circuit board image from a network, and after acquiring the sample circuit board image, label the sample circuit board image by using a rectangular enclosure frame, where the labeled area is an interference area in the sample circuit board image, for example, the interference area in the sample circuit board image is labeled as a category 1, and an area other than the interference object is labeled as a category 2.
In the embodiment, the terminal acquires the preset number of sample circuit board images; the method comprises the steps of marking a sample circuit board image to obtain a training sample, inputting the training sample into a target detection network for training to obtain an interferent recognition model, recognizing interferent areas in the circuit board image through the interferent recognition model, and further extracting electromigration areas, so that the electromigration areas on a circuit board device can be detected without manual work, and the efficiency and accuracy of electromigration area detection are improved.
In one embodiment, before the terminal performs the edge detection on the circuit board image, the terminal may further perform a preprocessing operation on the circuit board image, where the preprocessing operation includes image graying, morphological processing, and median filtering.
The graying is to convert a color picture into a grayscale picture and convert three-dimensional data into one-dimensional data, so that the later-stage computation amount is reduced and the computation complexity is reduced. Specifically, the following image graying formula is adopted in the present application to perform image graying operation on the circuit board image, and the image graying formula is as follows:
f(i,j)＝0.30R(i,j)+0.59G(i,j)+0.11B(i,j)
wherein f (i, j) is a pixel value of a pixel point with a coordinate (i, j) in the circuit board image after the image is grayed, and R (i, j), G (i, j) and B (i, j) are color values corresponding to R, G and B three color channels in an RGB color space of the pixel point with the coordinate (i, j) in the circuit board image before the image is grayed.
Morphological processing refers to extracting components of interest from image regions to delineate the most essential shapes and structures of the regions in the image, often used to enhance the image, extract connected components, fill in regions, and the like. The basic morphological processes include erosion, dilation, open operations and closed operations.
The expansion can enlarge the connected regions in the image, the shape and the size of the structural elements can influence the expansion result, and the expansion operation is mainly used for filling gaps and connecting disconnected connected regions; erosion can reduce connected domains in the image and ablate boundaries of the image structure, and likewise, the shape and size of the structural elements can affect the erosion result, and erosion operation is mainly used for disconnecting and eliminating tiny parts; the operation is to carry out corrosion on the image for one time and then carry out expansion for one time; the closed operation is to perform expansion and then corrosion on the image. The morphological processing performed on the circuit board image in the present application may be a closed operation, and the morphological expansion formula, the morphological erosion formula and the morphological closed operation formula specifically adopted are as follows:
wherein F is a circuit board image, b is forThe rows of morphologically expanded structural elements,
representing the morphological expansion of the pixel point with the coordinate (i, j) in the circuit board image,
the morphological corrosion is performed on the pixel point with the coordinate (i, j) in the circuit board image, and s and t are morphological processing offsets corresponding to i and j respectively.
The median filtering process is a non-linear smoothing technique for eliminating noise introduced during image acquisition and transmission. The formula used for median filtering is as follows:
g(i,j)＝med{f(i-k,j-l),(k,l∈W)}
wherein g (i, j) is a pixel value corresponding to a median filter of a pixel point with coordinates (i, j) in the circuit board image, k and l are filter offsets corresponding to i and j respectively, and W is a filter offset set.
In the above embodiment, before the terminal performs edge detection on the circuit board image, the noise can be effectively removed and the effective information of the image can be retained by performing preprocessing operation on the circuit board image, so that the calculated amount in the electromigration region detection process is reduced, and the electromigration region detection efficiency is improved.
In one embodiment, the terminal extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm comprises: converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space; and extracting an electromigration region in the circuit board image without the interference object in the preset color space by a visual saliency detection algorithm.
Specifically, a circuit board image acquired by a terminal is an image of an RGB color space, that is, a target circuit board image without an interfering object acquired by the terminal is also an image of the RGB color space, the terminal performs image space conversion on the target circuit board image after acquiring the target circuit board image, specifically, converts the target circuit board image from the RGB color space to an LAB color space, that is, converts the RGB target circuit board image to the target circuit board image of an LAB, after acquiring the target circuit board image of the LAB, the terminal may further perform gaussian kernel blurring on the target image of the LAB to obtain the blurred target circuit board image, calculates a color mean value of the target circuit board image in the LAB color space, calculates two norms between a color value and the color mean value of each pixel point in the blurred target circuit board image, generates a saliency map based on the two norms corresponding to each pixel point in the blurred target circuit board image, the electromigration regions are extracted from the saliency map. The calculation formula of the two norms is as follows:
S(i,j)＝||Iu-Iwhc(i,j)||2
in the above formula, S (I, j) is the two-norm corresponding to the pixel point with coordinate (I, j) in the target circuit board image after the blurring processing, IuColor mean of target circuit board image, I, for LABwhc(i, j) blurring the color value corresponding to the pixel point of (i, j) in the target circuit board image.
In the embodiment, the terminal converts the circuit board image without the interference object into the preset color space to obtain the circuit board image without the interference object in the preset color space, so that the circuit board image without the interference object is more quickly extracted in the preset color space through a visual saliency detection algorithm, and the efficiency of detecting the electromigration region is improved.
In an embodiment, after the terminal obtains the saliency map through a visual saliency detection algorithm, binarization and median filtering can be performed on the saliency map to obtain a processed saliency map, and the processed saliency map is used as a mask to register and overlay the processed saliency map and a circuit board image to obtain a detection result image of a significantly labeled electromigration region. The formula adopted for carrying out binarization on the saliency map is as follows:
wherein, value (i, j) is a corresponding gray value (color value) after binarization of a pixel point with coordinate (i, j) in the saliency map, and gray (i, j) is a corresponding gray value (color value) before binarization of a pixel point with coordinate (i, j) in the saliency map.
In the embodiment, the terminal performs binarization and median filtering on the saliency map to obtain a processed saliency map, and performs registration and superposition on the processed saliency map and the circuit board image by using the processed saliency map as a mask to obtain a detection result image with a significant electromigration region label, so that the position of the electromigration region in the circuit board image can be visually displayed.
In one embodiment, the terminal records the rotation angle of the prediction frame and the parameters of the prediction frame after regressing the prediction frame in the process of training the interferent identification model, wherein the parameters of the prediction frame comprise the center coordinates, the width and the height of the prediction frame, the preprocessing operation performed on the circuit board image further comprises performing first affine transformation on the circuit board image based on the parameters of the prediction frame to obtain the circuit board image after affine transformation, further using the interferent identification model to identify the interferent in the circuit board image after affine transformation, performing operations such as interferent removal and electromigration region extraction, and performing second affine transformation on the detection result image of the significant electromigration region based on the parameters of the prediction frame after obtaining the detection result image of the significant electromigration region, and obtaining a detection result image which is consistent with the circuit board image and obviously marks the electromigration region. Wherein the second affine transformation is the inverse of the first affine transformation. Fig. 5 is a diagram illustrating an exemplary detection result image of a significantly labeled electromigration region corresponding to an image of a circuit board.
In the embodiment, before the terminal identifies the interferent to the circuit board image, the image affine transformation is performed on the circuit board image based on the prediction frame parameters, so that the accuracy of identifying the interferent can be improved, and the accuracy of detecting the electromigration region is improved.
In one embodiment, after the terminal extracts the electromigration region in the target circuit board image through a visual saliency detection algorithm, the terminal may further calculate an area of the electromigration region in the circuit board image, determine a short circuit risk level of the circuit board corresponding to the circuit board image according to the area, generate risk prompt information corresponding to the short circuit risk level, and prompt the risk prompt information. The ratio of the area of the electromigration region in the circuit board image to the area of the circuit board image is larger, and the corresponding short circuit risk level is higher.
In the above embodiment, the terminal determines the short circuit risk level of the circuit board corresponding to the circuit board image according to the area by calculating the area of the electromigration region in the circuit board image, and generates and prompts risk prompt information corresponding to the short circuit risk level, so that the circuit board is prevented from being continuously used when the short circuit risk of the circuit board is high, and potential safety hazards are reduced.
In an embodiment, as shown in fig. 6, there is further provided an electromigration region detection method, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
and S602, acquiring a circuit board image.
S604, preprocessing the circuit board image to obtain a preprocessed circuit board image; the preprocessing operation comprises image graying, morphological processing and median filtering processing.
And S606, performing first image affine transformation on the preprocessed image to obtain a circuit board image after the first image affine transformation.
And S608, performing edge detection on the circuit board image after the first image affine transformation to obtain edge information of the circuit board image.
S610, identifying the interferent in the circuit board image based on the edge information.
And S612, removing the interference object from the circuit board image to obtain a target circuit board image without the interference object.
And S614, converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space.
S616, extracting the electromigration region in the circuit board image without the interference object in the preset color space through a visual saliency detection algorithm, and generating a corresponding saliency map.
And S618, registering and superposing the saliency map and the circuit board image to obtain a detection result image with the electromigration region marked obviously.
S620, carrying out second affine transformation on the detection result image marked with the electromigration region to obtain a detection result image marked with the electromigration region and consistent with the circuit board image.
It should be understood that although the various steps in the flowcharts of fig. 2 and 6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 and 6 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 7, there is provided an electromigration region detection apparatus, including: an image acquisition module 702, an edge detection module 704, an image recognition module 706, an interferent removal module 708, and an electromigration region extraction module 710, wherein:
an image acquisition module 702, configured to acquire a circuit board image;
an edge detection module 704, configured to perform edge detection on the circuit board image to obtain edge information of the circuit board image;
the image identification module 706 is used for identifying interferents in the circuit board image based on the edge information;
the interferent removing module 708 is configured to remove interferents from the circuit board image to obtain a target circuit board image without interferents;
and the electromigration region extraction module 710 is used for extracting an electromigration region in the target circuit board image through a visual saliency detection algorithm.
In the embodiment, after the terminal acquires the circuit board image, the edge of the circuit board image is detected to obtain the edge information of the circuit board image; identifying interferents in the circuit board image based on the edge information; removing the interferent from the circuit board image to obtain a target circuit board image without the interferent; the electromigration region in the target circuit board image is extracted through a visual saliency detection algorithm, so that the electromigration region on a circuit board device can be detected without manual work, and the efficiency and the accuracy of electromigration region detection are improved.
In one embodiment, as shown in fig. 8, the apparatus further comprises: a sample circuit board image acquisition module 712, a labeling module 714, and a model training module 716, wherein:
a sample circuit board image obtaining module 712, configured to obtain a preset number of sample circuit board images;
the marking module 714 is used for marking the sample circuit board image to obtain a training sample;
the model training module 716 is configured to input a training sample into the target detection network for training to obtain an interferent identification model;
an image recognition module 706, further configured to:
and identifying the interferent in the circuit board image based on the edge information through the interferent identification model.
In the embodiment, the terminal acquires the preset number of sample circuit board images; the method comprises the steps of marking a sample circuit board image to obtain a training sample, inputting the training sample into a target detection network for training to obtain an interferent recognition model, recognizing interferent areas in the circuit board image through the interferent recognition model, and further extracting electromigration areas, so that the electromigration areas on a circuit board device can be detected without manual work, and the efficiency and accuracy of electromigration area detection are improved.
In one embodiment, the apparatus further comprises:
and the preprocessing module is used for preprocessing the circuit board image, and the preprocessing operation comprises image graying, morphological processing and median filtering processing.
In the above embodiment, before the terminal performs edge detection on the circuit board image, the noise can be effectively removed and the effective information of the image can be retained by performing preprocessing operation on the circuit board image, so that the calculated amount in the electromigration region detection process is reduced, and the electromigration region detection efficiency is improved.
In one embodiment, the electromigration region extraction module 710 is further configured to:
converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space;
and extracting an electromigration region in the circuit board image without the interference object in the preset color space by a visual saliency detection algorithm.
In the embodiment, the terminal converts the circuit board image without the interference object into the preset color space to obtain the circuit board image without the interference object in the preset color space, so that the circuit board image without the interference object is more quickly extracted in the preset color space through a visual saliency detection algorithm, and the efficiency of detecting the electromigration region is improved.
In one embodiment, the apparatus further comprises:
the area calculation module is used for calculating the area of the electromigration region in the circuit board image;
the short circuit risk grade determining module is used for determining the short circuit risk grade of the circuit board corresponding to the circuit board image according to the area;
and the prompt module is used for generating risk prompt information corresponding to the short circuit risk level and prompting.
In the above embodiment, the terminal determines the short circuit risk level of the circuit board corresponding to the circuit board image according to the area by calculating the area of the electromigration region in the circuit board image, and generates and prompts risk prompt information corresponding to the short circuit risk level, so that the circuit board is prevented from being continuously used when the short circuit risk of the circuit board is high, and potential safety hazards are reduced.
The specific definition of the electromigration region detection apparatus can be referred to the definition of the electromigration region detection method in the above, and is not described herein again. The modules in the electromigration region detection apparatus can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing circuit board image data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of electromigration region detection.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of electromigration region detection. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the configurations shown in fig. 9 or 10 are block diagrams of only some of the configurations relevant to the present application, and do not constitute a limitation on the computing devices to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of: acquiring a circuit board image; performing edge detection on the circuit board image to obtain edge information of the circuit board image; identifying interferents in the circuit board image based on the edge information; removing the interferent from the circuit board image to obtain a target circuit board image without the interferent; and extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of: acquiring a preset number of sample circuit board images; marking the sample circuit board image to obtain a training sample; inputting the training sample into a target detection network for training to obtain an interferent identification model; the computer program, when executed by the processor, causes the processor to perform the step of identifying an interfering object in the circuit board image based on the edge information, further comprising the steps of: and identifying the interferent in the circuit board image based on the edge information through the interferent identification model.
In one embodiment, the computer program, when executed by the processor, causes the processor to further perform the steps of: and carrying out preprocessing operation on the circuit board image, wherein the preprocessing operation comprises image graying, morphological processing and median filtering processing.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of extracting an electromigration region in the target circuit board image by a visual saliency detection algorithm, the step of: converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space; and extracting an electromigration region in the circuit board image without the interference object in the preset color space by a visual saliency detection algorithm.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of: calculating the area of an electromigration region in the circuit board image; determining the short circuit risk level of the circuit board corresponding to the circuit board image according to the area; and generating risk prompt information corresponding to the short circuit risk level and prompting.
In one embodiment, a computer readable storage medium is provided, storing a computer program that, when executed by a processor, causes the processor to perform the steps of: acquiring a circuit board image; performing edge detection on the circuit board image to obtain edge information of the circuit board image; identifying interferents in the circuit board image based on the edge information; removing the interferent from the circuit board image to obtain a target circuit board image without the interferent; and extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of: acquiring a preset number of sample circuit board images; marking the sample circuit board image to obtain a training sample; inputting the training sample into a target detection network for training to obtain an interferent identification model; the computer program, when executed by the processor, causes the processor to perform the step of identifying an interfering object in the circuit board image based on the edge information, further comprising the steps of: and identifying the interferent in the circuit board image based on the edge information through the interferent identification model.
In one embodiment, the computer program, when executed by the processor, causes the processor to further perform the steps of: and carrying out preprocessing operation on the circuit board image, wherein the preprocessing operation comprises image graying, morphological processing and median filtering processing.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the step of extracting an electromigration region in the target circuit board image by a visual saliency detection algorithm, the step of: converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space; and extracting an electromigration region in the circuit board image without the interference object in the preset color space by a visual saliency detection algorithm.
In one embodiment, the computer program, when executed by the processor, causes the processor to perform the steps of: calculating the area of an electromigration region in the circuit board image; determining the short circuit risk level of the circuit board corresponding to the circuit board image according to the area; and generating risk prompt information corresponding to the short circuit risk level and prompting.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An electromigration region detection method, the method comprising:
acquiring a circuit board image;
performing edge detection on the circuit board image to obtain edge information of the circuit board image;
identifying interferents in the circuit board image based on the edge information;
removing the interference object from the circuit board image to obtain a target circuit board image without the interference object;
and extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm.
2. The method of claim 1, further comprising:
acquiring a preset number of sample circuit board images;
marking the sample circuit board image to obtain a training sample;
inputting the training sample into a target detection network for training to obtain an interferent recognition model;
the identifying the interferent in the circuit board image based on the edge information includes:
and identifying the interferent in the circuit board image based on the edge information through the interferent identification model.
3. The method of claim 1, wherein prior to performing edge detection on the circuit board image, the method further comprises:
and carrying out preprocessing operation on the circuit board image, wherein the preprocessing operation comprises image graying, morphological processing and median filtering processing.
4. The method of claim 1, wherein said extracting, by a visual saliency detection algorithm, electromigration regions in said target circuit board image comprises:
converting the circuit board image without the interference object into a preset color space to obtain the circuit board image without the interference object in the preset color space;
and extracting an electromigration region in the circuit board image without the interference object in the preset color space through a visual saliency detection algorithm.
5. The method of claim 1, wherein after extracting the electromigration region in the target circuit board image by a visual saliency detection algorithm, the method further comprises:
calculating the area of an electromigration region in the circuit board image;
determining the short circuit risk level of the circuit board corresponding to the circuit board image according to the area;
and generating risk prompt information corresponding to the short circuit risk level and prompting.
6. An electromigration region detection apparatus, the apparatus comprising:
the image acquisition module is used for acquiring a circuit board image;
the edge detection module is used for carrying out edge detection on the circuit board image to obtain edge information of the circuit board image;
the image identification module is used for identifying interferents in the circuit board image based on the edge information;
the interference object removing module is used for removing the interference object from the circuit board image to obtain a target circuit board image without the interference object;
and the electromigration region extraction module is used for extracting the electromigration region in the target circuit board image through a visual saliency detection algorithm.
7. The apparatus of claim 6, further comprising:
the sample circuit board image acquisition module is used for acquiring a preset number of sample circuit board images;
the marking module is used for marking the sample circuit board image to obtain a training sample;
inputting the training sample into a target detection network for training to obtain an interferent recognition model;
the identifying the interferent in the circuit board image based on the edge information includes:
and identifying the interferent in the circuit board image based on the edge information through the interferent identification model.
8. The apparatus of claim 6, further comprising:
and the preprocessing module is used for preprocessing the circuit board image, and the preprocessing operation comprises image graying, morphological processing and median filtering processing.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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