CN109978962B - Low-contrast indicating value image intelligent identification method for darkroom illuminometer calibration - Google Patents
Low-contrast indicating value image intelligent identification method for darkroom illuminometer calibration Download PDFInfo
- Publication number
- CN109978962B CN109978962B CN201910280450.3A CN201910280450A CN109978962B CN 109978962 B CN109978962 B CN 109978962B CN 201910280450 A CN201910280450 A CN 201910280450A CN 109978962 B CN109978962 B CN 109978962B
- Authority
- CN
- China
- Prior art keywords
- contrast
- image
- low
- darkroom
- num
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000001514 detection method Methods 0.000 claims abstract description 17
- 238000013527 convolutional neural network Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000005286 illumination Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 2
- 238000012795 verification Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012015 optical character recognition Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a darkroom illuminometer calibration-oriented intelligent identification method for low-contrast indicating value images, which comprises the following steps: respectively performing data enhancement on the high-contrast image and the low-contrast image to complete model training of indication value image prediction of the CNN target detection frame under different contrasts; calculating the optimal recognition contrast according to the prediction probability and the contrast of the correct prediction sample in the training process; in the actual prediction process, the image contrast is enhanced to the optimal recognition contrast, and the recognition accuracy is further improved. The method realizes the identification of the illuminance indicating value in the low-contrast environment and has good applicability.
Description
Technical Field
The invention relates to the field of machine vision, in particular to an intelligent identification method for illuminance meter indication values in a low-illumination environment.
Background
In the illuminometer calibration industry, since the illuminometer calibration needs to be carried out in a darkroom and under the condition of no stray light interference, the difficulty of calibrating personnel in identifying the indication value of the illuminometer in a low-light environment is greatly increased, and meanwhile, fatigue work is easily caused to influence the identification accuracy; on the other hand, working in a darkroom with severely limited light can easily cause health and safety problems for the certified personnel. The widely used optical character recognition method at present needs to have good illumination conditions to accurately recognize characters in an image, which conflicts with the environment of illuminometer verification, so that the method for efficiently, accurately and intelligently recognizing the illuminometer indication value in the verification process has important practical significance.
Disclosure of Invention
In order to solve the existing problems, the invention adopts a Convolutional Neural Network (CNN) target detection framework, trains the CNN target detection framework by constructing a data set and a data enhancement algorithm, searches for the optimal prediction contrast and realizes the accurate identification of the illuminometer readings in the low-contrast environment.
The purpose of the invention is realized by the following technical scheme:
a darkroom illuminometer calibration-oriented intelligent low-contrast indicating image identification method comprises the following steps:
A. making a training and testing data set of a convolutional neural network target detection framework, wherein the training data set comprises a high-contrast nixie tube image set IHAnd low contrast nixie tube image set ILAnd calculate IHMinimum value of contrast C for collective imageH_min、ILMaximum value of contrast C of collective imageL_max;
B. Calculating image contrast C in CNN target detection frame at each input during training and testingtrJudging the set to which the image belongs and changing the contrast of the input image;
C. the CNN target detection framework generates a prediction target frame and a probability P output for an input image, and after training is finished, the optimal recognition contrast C is calculated according to a P-C curvebest;
D. Calculating low contrast illuminometer representative image contrast C in darkroom predicted by CNN target detection frameworkrealAnd performing enhancement.
According to the method, data enhancement is respectively carried out on the high-contrast image and the low-contrast image, model training of the CNN target detection frame in value image prediction under different contrasts is completed, and the optimal recognition contrast is calculated according to the prediction probability and the contrast of a correct prediction sample in the training process. In the actual prediction process, the image contrast is enhanced to the optimal recognition contrast, and the recognition accuracy is further improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of the structure of the method for intelligently identifying a low-contrast indicating image for darkroom illuminometer calibration according to the present invention.
Detailed Description
According to the technical scheme of the invention, a plurality of structural modes and manufacturing methods of the invention can be provided by a person with ordinary skill in the art without changing the essential spirit of the invention. Therefore, the following detailed description and the accompanying drawings are only specific illustrations of the technical solutions of the present invention, and should not be construed as all of the present invention or as limitations or limitations on the technical solutions of the present invention.
The invention is described in further detail below with reference to examples and figures:
as shown in fig. 1, a darkroom illuminometer calibration-oriented intelligent identification method for low-contrast indicating value images comprises the following specific steps:
the step 10 specifically includes: i isHImage dataset is { FH,1,FH,2,FH,3,……,FH,Num_H},ILImage dataset is { FL,1,FL,2,FL,3,……,FL,Num_LWherein Num _ H and Num _ L are I respectivelyH、ILNumber of middle images, F ═ Fijk)m×n×3As a RGB color space mapLike a matrix. Converting RGB image into grayscale image F in calculating image contrastgray=(fij)m×nThen, the method for calculating the contrast C of the image is as follows:
i can be calculated according to the formulaHAnd ILSet of contrasts { C over all images in training datasetH,1,CH,2,CH,2,……,CH,Num_HAnd { C }L,1,CL,2,CL,2,……,CL,Num_LGet CH_minAnd CL_maxAnd by adjusting the set IHAnd ILGuarantee CH_min>CL_max。
The step 20 specifically includes: calculating a training image F according to an image contrast calculation formulatrContrast ratio C oftrThen F istrThe method for judging the belonged set comprises the following steps:
Ftrthe data enhancement method comprises the following steps:
wherein s is2For contrast enhancement factor, the function random (x, y) acts to randomly generate a bit in the interval [ x, y ]]Of (4).
The step 30 specifically includes: selecting a part for correctly predicting the indicating value of the nixie tube image in the whole training process, wherein the contrast of each training image is { Cright,1,Cright,2,Crhght,3,……,Cright,Num_rightWith a corresponding prediction probability of { P }right,1,Pright,2,Pright,3,……,Pright,Num_rightNum _ right is to make the correct predictionMeasuring the number of fractions, then CbestThe calculation method comprises the following steps:
the step 40 specifically includes: contrast C from images captured by industrial cameras before actual recognition of light level indications in low light environmentsrealContrast enhancement is performed with a contrast enhancement factor of
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A darkroom illuminometer calibration-oriented intelligent identification method for low-contrast indicating value images is characterized by comprising the following specific steps:
A. making a training and testing data set of a convolutional neural network target detection framework, wherein the training data set comprises a high-contrast nixie tube image set IHAnd low contrast nixie tube image set ILAnd calculate IHMinimum value of set image contrast CH_min、ILMaximum value of contrast C of collective imageL_max;
B. Calculating image contrast C in CNN target detection frame at each input during training and testingtrJudging the set to which the image belongs and changing the contrast of the input image by using a corresponding data enhancement algorithm;
C. the CNN target detection framework generates a prediction target frame and probability P output for an input image, and after training is finished, the probability is predicted according to the imageP and the contrast C of the image, and calculating the optimal recognition contrast Cbest;
D. Calculating low contrast illuminometer representative image contrast C in darkroom predicted by CNN target detection frameworkrealAnd performing enhancement;
in the step C, a part for correctly predicting the numerical tube image indicating value in the whole training process is selected, and the contrast of each training image is { Cright,1,Cright,2,Crhght,3,……,Cright,Num_rightCorresponding to a prediction probability of { P }right,1,Pright,2,Pright,3,……,Pright,Num_rightNum _ right is the number of parts to make the correct prediction, then CbestThe calculation method comprises the following steps:
2. the darkroom illuminometer-certification-oriented intelligent identification method for low-contrast indicating images according to claim 1, wherein in the step A, IHImage dataset is { FH,1,FH,2,FH,3,……,FH,Num_H},ILImage dataset is { FL,1,FL,2,FL,3,……,FL,Num_LH, Num _ H and Num _ L are IH、ILNumber of middle images, F ═ Fijk)m×n×3Is an RGB color space image matrix; converting RGB image into grayscale image F in calculating image contrastgray=(fij)m×nThen, the method for calculating the contrast C of the image is as follows:
i can be calculated according to a formulaHAnd ILSet of contrasts { C over all images in training datasetH,1,CH,2,CH,2,……,CH,Num_HAnd { C }L,1,CL,2,CL,2,……,CL,Num_LGet CH_minAnd CL_maxAnd by adjusting the set IHAnd ILGuarantee CH_min>CL_max。
3. The darkroom illuminometer-certification-oriented intelligent identification method for low-contrast indicating-value images according to claim 1, wherein in the step B, the training image F is calculated according to an image contrast calculation formulatrContrast ratio C oftrThen F istrThe method for judging the belonged set comprises the following steps:
5. The method for intelligently identifying low-contrast-indicated-image for darkroom illuminometer calibration according to claim 1, wherein in the step D, before actually identifying the illuminometer indication in the low-illumination environment, the contrast C of the low-contrast-indicated-image in the darkroom is predicted according to the actually used CNN target detection frameworkrealContrast enhancement is carried out with a contrast enhancement factor ofCbestFor low contrast in darkroomThe illuminance representation image optimally identifies the contrast.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910280450.3A CN109978962B (en) | 2019-04-09 | 2019-04-09 | Low-contrast indicating value image intelligent identification method for darkroom illuminometer calibration |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910280450.3A CN109978962B (en) | 2019-04-09 | 2019-04-09 | Low-contrast indicating value image intelligent identification method for darkroom illuminometer calibration |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109978962A CN109978962A (en) | 2019-07-05 |
CN109978962B true CN109978962B (en) | 2022-05-17 |
Family
ID=67083664
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910280450.3A Active CN109978962B (en) | 2019-04-09 | 2019-04-09 | Low-contrast indicating value image intelligent identification method for darkroom illuminometer calibration |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109978962B (en) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN117593766B (en) * | 2023-12-28 | 2024-04-02 | 天科院环境科技发展(天津)有限公司 | Investigation method for wild animal population number based on unmanned aerial vehicle shooting image processing |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105184747A (en) * | 2015-09-09 | 2015-12-23 | 天津光电高斯通信工程技术股份有限公司 | Low illumination image contrast improvement method |
CN106169081A (en) * | 2016-06-29 | 2016-11-30 | 北京工业大学 | A kind of image classification based on different illumination and processing method |
CN109242788A (en) * | 2018-08-21 | 2019-01-18 | 福州大学 | One kind being based on coding-decoding convolutional neural networks low-light (level) image optimization method |
CN109523474A (en) * | 2018-10-19 | 2019-03-26 | 福州大学 | A kind of enhancement method of low-illumination image based on greasy weather degradation model |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR20160032587A (en) * | 2014-09-16 | 2016-03-24 | 삼성전자주식회사 | Image photographing apparatus and photographing method thereof |
-
2019
- 2019-04-09 CN CN201910280450.3A patent/CN109978962B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105184747A (en) * | 2015-09-09 | 2015-12-23 | 天津光电高斯通信工程技术股份有限公司 | Low illumination image contrast improvement method |
CN106169081A (en) * | 2016-06-29 | 2016-11-30 | 北京工业大学 | A kind of image classification based on different illumination and processing method |
CN109242788A (en) * | 2018-08-21 | 2019-01-18 | 福州大学 | One kind being based on coding-decoding convolutional neural networks low-light (level) image optimization method |
CN109523474A (en) * | 2018-10-19 | 2019-03-26 | 福州大学 | A kind of enhancement method of low-illumination image based on greasy weather degradation model |
Non-Patent Citations (4)
Title |
---|
LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement;ChongyiLi et al.;《Pattern Recognition Letters》;20180116;第104卷;第15-22页 * |
图像对比度计算;lien0906;《CSDN》;20141103;第1-3页 * |
基于深度卷积神经网络的低照度图像增强;马红强 等;《光学学报》;20181007;第39卷(第2期);第1-10页 * |
复杂环境下的机器人视觉图像增强算法的研究;马金荣;《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》;20180515(第5期);第I138-389页 * |
Also Published As
Publication number | Publication date |
---|---|
CN109978962A (en) | 2019-07-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106875373B (en) | Mobile phone screen MURA defect detection method based on convolutional neural network pruning algorithm | |
CN108090902B (en) | Non-reference image quality objective evaluation method based on multi-scale generation countermeasure network | |
TWI742382B (en) | Neural network system for vehicle parts recognition executed by computer, method for vehicle part recognition through neural network system, device and computing equipment for vehicle part recognition | |
US20210174149A1 (en) | Feature fusion and dense connection-based method for infrared plane object detection | |
CN111814850A (en) | Defect detection model training method, defect detection method and related device | |
CN101819024B (en) | Machine vision-based two-dimensional displacement detection method | |
CN113298780B (en) | Deep learning-based bone age assessment method and system for children | |
CN116703911B (en) | LED lamp production quality detecting system | |
CN115994907B (en) | Intelligent processing system and method for comprehensive information of food detection mechanism | |
CN114022446A (en) | Leather flaw detection method and system based on improved YOLOv3 | |
CN109978962B (en) | Low-contrast indicating value image intelligent identification method for darkroom illuminometer calibration | |
CN113327243B (en) | PAD light guide plate defect visual detection method based on Ayolov3-Tiny new framework | |
CN112270404A (en) | Detection structure and method for bulge defect of fastener product based on ResNet64 network | |
CN117058079A (en) | Thyroid imaging image automatic diagnosis method based on improved ResNet model | |
CN109886923A (en) | It is a kind of for internet detection in measurement detection system and method based on machine learning | |
CN115482227A (en) | Machine vision self-adaptive imaging environment adjusting method | |
CN112419269B (en) | Pavement disease segmentation method | |
CN112730437B (en) | Spinneret plate surface defect detection method and device based on depth separable convolutional neural network, storage medium and equipment | |
CN114548250A (en) | Mobile phone appearance detection method and device based on data analysis | |
CN112801013A (en) | Face recognition method, system and device based on key point recognition and verification | |
CN113177074B (en) | Optical performance monitoring system and method for improving environmental adaptability | |
CN112712004B (en) | Face detection system, face detection method and device and electronic equipment | |
CN116246209B (en) | Wide-angle lens biological target detection method based on offset convolution kernel | |
CN112816408B (en) | Flaw detection method for optical lens | |
CN115995046B (en) | Rural road remote sensing extraction method and device under shadow shielding state |
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 |