CN113327237A - Visual detection system suitable for power supply circuit board - Google Patents
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Abstract
The invention relates to a visual detection system, in particular to a visual detection system suitable for a power supply circuit board, which can effectively extract a corresponding detection area in a processed circuit board image according to a detection task through a detection area segmentation model, and a defect detection module compares the extracted detection area with a standardized circuit board image for defect detection, so that the detection area can be accurately extracted according to the detection task, and the detection precision is ensured; the region to be identified can be marked in the nameplate image through the region to be identified extraction model, the character identification module carries out character identification on the marked region to be identified, and the power supply model and the power supply number of the power supply to be detected are obtained, so that effective character identification can be carried out on the nameplate of the power supply to be detected; the technical scheme provided by the invention can effectively overcome the defects of poor detection precision and incapability of carrying out character recognition on the power supply nameplate in the prior art.
Description
Technical Field
The invention relates to a visual detection system, in particular to a visual detection system suitable for a power circuit board.
Background
The power supply comprehensive tester can complete the machine installation and electric installation inspection work of a power supply machine in a continuous production mode. At present, a power supply comprehensive tester mainly comprises a machine vision identification module, an insulation performance testing module and an electrical performance testing module, and detection tasks required by daily production can be completed through the modules.
Circuit boards, also known as printed wiring boards or printed circuit boards, play an important role in modern electronic equipment. The detection of the defects of the circuit board is a key means for evaluating the quality of the circuit board, and directly influences the performance and the use safety of a power supply. Traditionally, circuit board defect detection is generally accomplished by the manual work, but this part work is boring, and people's energy is limited moreover, easily takes place to miss the circumstances of examining and false retrieval, and detection efficiency is not high. Meanwhile, as the internet technology is accelerated to change from a production tool to a production element, the combination of the internet technology and the traditional industry is increasingly compact, the industry is mainly embodied in the combination of the internet and the manufacturing industry, namely 'internet + manufacturing', and the automatic detection of the defects of the circuit board through machine vision is an urgent need in the industry and is an inevitable development trend.
However, the existing visual detection system for the circuit board has the defects of poor detection precision, poor detection effect, missed detection, incapability of performing character recognition on a power supply nameplate, incapability of judging the power supply model and the power supply number of a power supply to be detected and inconvenience in detection.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a visual detection system suitable for a power supply circuit board, which can effectively overcome the defects of poor detection precision and incapability of carrying out character recognition on a power supply nameplate in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a visual inspection system suitable for power supply circuit board comprises a controller, and
the standard information input module is used for inputting a standard circuit board image and the model of the power supply to be detected;
the image processing module is used for carrying out image processing on the circuit board image acquired by the first detection image acquisition module;
the detection item selection module is used for selecting a circuit board detection task;
the detection region segmentation model is used for effectively extracting a corresponding detection region in the processed circuit board image according to the detection task;
the defect detection module is used for comparing the extracted detection area with the standardized circuit board image and searching for defects;
the manual labeling module is used for manually labeling the to-be-identified area in the standardized nameplate image acquired by the training image acquisition module;
extracting a model of the region to be identified, performing model training by manually marking a standardized nameplate image of the region to be identified, and marking the region to be identified in the nameplate image acquired by the second detection image acquisition module;
the character recognition module is used for carrying out character recognition on the to-be-recognized area marked by the to-be-recognized area extraction model;
and the data comparison module is used for comparing the character recognition result obtained by the character recognition module with the model of the power supply to be tested.
Preferably, the detection region segmentation model effectively extracts the corresponding detection region in the processed circuit board image through model training, and includes:
collecting a plurality of standardized circuit board images, manually marking corresponding detection areas in the circuit board images by adopting a rectangular frame according to different circuit board detection tasks, and establishing a training data set;
inputting the training data set into the detection region segmentation model for model training to obtain a trained detection region segmentation model;
and inputting the processed circuit board image into a detection area segmentation model, and marking the corresponding detection area in the processed circuit board image by the detection area segmentation model according to the detection task.
Preferably, the image processing module performs image enhancement, image noise reduction and image extraction on the circuit board image, wherein
Image enhancement: selecting a gray level transformation area, modifying the gray level value of the image through linear operation, and carrying out gray level transformation on the gray level transformation area;
image denoising: selecting a noise reduction window by taking a target pixel as a center, calculating an average value of all pixels in the noise reduction window to replace an original pixel value, keeping the shape and the size of the window, and replacing each pixel in the image by the average value by taking the pixel as the center;
image extraction: and determining the image edge by detecting the position of the image gray value step change, and extracting the processed circuit board image according to the image edge.
Preferably, the detection item selection module is used for selecting one or more detection tasks from screws, cables, inductors, fuses, electrolytic capacitors and PCB boards.
Preferably, the defect detection module performs defect detection on the circuit board image marked with the detection area and the corresponding area in the standardized circuit board image through pixel-by-pixel comparison.
Preferably, the method further comprises the following steps:
the detection area self-defining module is used for self-defining and marking a detection area in the standardized circuit board image;
the detection information statistic module is used for counting the defect detection result of the defect detection module;
and the detection result display module is used for displaying the related detection result of the circuit board detection task and the statistical result of the detection information statistical module.
Preferably, the character recognition module performs character recognition on the to-be-recognized region labeled by the to-be-recognized region extraction model, and includes:
acquiring a nameplate image marked with a region to be recognized, performing OCR recognition, and inputting OCR recognition results into a language model one by one to obtain an OCR output sequence set;
and converting the output sequences in the OCR output sequence set into digital vectors one by one, performing dimensionality reduction processing, and inputting the dimensionality reduced digital vectors into a recurrent neural network one by one to obtain a text sequence.
Preferably, the model for extracting the region to be recognized is trained by manually labeling the standardized nameplate image of the region to be recognized, and the method comprises the following steps:
manually labeling areas to be identified, including areas where power supply types and power supply numbers are located, in the standardized nameplate images at different angles and under different illumination conditions, and establishing a training data set;
and inputting the training data set into the region to be recognized extraction model for model training to obtain the trained region to be recognized extraction model.
Preferably, the robot further comprises a robot arm control module, which is used for controlling the robot arm according to the defect detection result of the defect detection module and the comparison result of the data comparison module;
when the defect detection result of the defect detection module is lower than the detection result of the corresponding detection task of the good product, or the data comparison module compares and judges that the power model identified by the character identification module is inconsistent with the power model to be detected, the controller controls the mechanical arm to clamp the corresponding circuit board to be detected to the unqualified product through the mechanical arm control module.
(III) advantageous effects
Compared with the prior art, the visual detection system applicable to the power circuit board provided by the invention has the following advantages:
1) the detection region segmentation model can effectively extract the corresponding detection region in the processed circuit board image according to the detection task, and the defect detection module compares the extracted detection region with the standardized circuit board image to detect the defect, so that the detection region can be accurately extracted according to the detection task, and the detection precision is ensured;
2) the region to be identified can be marked in the nameplate image through the region to be identified extraction model, the character recognition module performs character recognition on the marked region to be identified, and the power model and the power serial number of the power source to be detected are obtained, so that effective character recognition can be performed on the nameplate of the power source to be detected.
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 invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all 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.
A visual inspection system suitable for a power circuit board, as shown in FIG. 1, includes a controller, an
The standard information input module is used for inputting a standard circuit board image and the model of the power supply to be detected;
the image processing module is used for carrying out image processing on the circuit board image acquired by the first detection image acquisition module;
the detection item selection module is used for selecting a circuit board detection task;
the detection region segmentation model is used for effectively extracting a corresponding detection region in the processed circuit board image according to the detection task;
the defect detection module is used for comparing the extracted detection area with the standardized circuit board image and searching for defects;
the manual labeling module is used for manually labeling the to-be-identified area in the standardized nameplate image acquired by the training image acquisition module;
extracting a model of the region to be identified, performing model training by manually marking a standardized nameplate image of the region to be identified, and marking the region to be identified in the nameplate image acquired by the second detection image acquisition module;
the character recognition module is used for carrying out character recognition on the to-be-recognized area marked by the to-be-recognized area extraction model;
and the data comparison module is used for comparing the character recognition result obtained by the character recognition module with the model of the power supply to be tested.
The image processing module performs image enhancement, image noise reduction and image extraction on the circuit board image, wherein
Image enhancement: selecting a gray level transformation area, modifying the gray level value of the image through linear operation, and carrying out gray level transformation on the gray level transformation area;
image denoising: selecting a noise reduction window by taking a target pixel as a center, calculating an average value of all pixels in the noise reduction window to replace an original pixel value, keeping the shape and the size of the window, and replacing each pixel in the image by the average value by taking the pixel as the center;
image extraction: and determining the image edge by detecting the position of the image gray value step change, and extracting the processed circuit board image according to the image edge.
In the technical scheme, the detection item selection module is used for selecting one or more detection tasks from screws, flat cables, inductors, fuses, electrolytic capacitors and PCB boards.
The detection area segmentation model effectively extracts the corresponding detection area in the processed circuit board image through model training, and the method comprises the following steps:
collecting a plurality of standardized circuit board images, manually marking corresponding detection areas in the circuit board images by adopting a rectangular frame according to different circuit board detection tasks, and establishing a training data set;
inputting the training data set into the detection region segmentation model for model training to obtain a trained detection region segmentation model;
and inputting the processed circuit board image into a detection area segmentation model, and marking the corresponding detection area in the processed circuit board image by the detection area segmentation model according to the detection task.
And the defect detection module carries out defect detection on the circuit board image marked with the detection area and the corresponding area in the standardized circuit board image through pixel-by-pixel comparison.
In the technical scheme of this application, still include:
the detection area self-defining module is used for self-defining and marking a detection area in the standardized circuit board image;
the detection information statistic module is used for counting the defect detection result of the defect detection module;
and the detection result display module is used for displaying the related detection result of the circuit board detection task and the statistical result of the detection information statistical module.
When a user customizes a label detection area in a standardized circuit board image through a detection area customization module, a detection area segmentation model directly extracts a corresponding detection area from the processed circuit board image according to the customized label detection area, and a defect detection module carries out defect detection through pixel-by-pixel comparison, so that the setting of the detection area is more flexible.
The model training is carried out to the standardized data plate image of waiting to discern the regional extraction model through artifical mark and waiting to discern the region, includes:
manually labeling areas to be identified, including areas where power supply types and power supply numbers are located, in the standardized nameplate images at different angles and under different illumination conditions, and establishing a training data set;
and inputting the training data set into the region to be recognized extraction model for model training to obtain the trained region to be recognized extraction model.
The character recognition module carries out character recognition on the region to be recognized marked by the extraction model of the region to be recognized, and the character recognition method comprises the following steps:
acquiring a nameplate image marked with a region to be recognized, performing OCR recognition, and inputting OCR recognition results into a language model one by one to obtain an OCR output sequence set;
and converting the output sequences in the OCR output sequence set into digital vectors one by one, performing dimensionality reduction processing, and inputting the dimensionality reduced digital vectors into a recurrent neural network one by one to obtain a text sequence.
The method comprises the following steps of inputting the dimensionality-reduced digital vectors into a recurrent neural network one by one to obtain a text sequence, wherein the step of inputting the dimensionality-reduced digital vectors into the recurrent neural network one by one to obtain the text sequence comprises the following steps:
inputting the dimensionality reduced digital vector into a Bi-LSTM encoder to generate a feature vector, and inputting the feature vector into a Bi-LSTM decoder to obtain an output vector;
and inputting the output vector into a Softmax algorithm module to obtain a word ID, and converting the word ID into a text sequence according to the corresponding relation of the dictionary.
In the technical scheme, the robot arm control device further comprises a robot arm control module for controlling the robot arm according to the defect detection result of the defect detection module and the comparison result of the data comparison module.
When the defect detection result of the defect detection module is lower than the detection result of the corresponding detection task of the good product, or the data comparison module compares and judges that the power model identified by the character identification module is inconsistent with the power model to be detected, the controller controls the mechanical arm to clamp the corresponding circuit board to be detected to the unqualified product through the mechanical arm control module.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (9)
1. The utility model provides a visual inspection system suitable for power supply circuit board which characterized in that: comprises a controller, and
the standard information input module is used for inputting a standard circuit board image and the model of the power supply to be detected;
the image processing module is used for carrying out image processing on the circuit board image acquired by the first detection image acquisition module;
the detection item selection module is used for selecting a circuit board detection task;
the detection region segmentation model is used for effectively extracting a corresponding detection region in the processed circuit board image according to the detection task;
the defect detection module is used for comparing the extracted detection area with the standardized circuit board image and searching for defects;
the manual labeling module is used for manually labeling the to-be-identified area in the standardized nameplate image acquired by the training image acquisition module;
extracting a model of the region to be identified, performing model training by manually marking a standardized nameplate image of the region to be identified, and marking the region to be identified in the nameplate image acquired by the second detection image acquisition module;
the character recognition module is used for carrying out character recognition on the to-be-recognized area marked by the to-be-recognized area extraction model;
and the data comparison module is used for comparing the character recognition result obtained by the character recognition module with the model of the power supply to be tested.
2. The visual inspection system for a power circuit board of claim 1, wherein: the detection area segmentation model effectively extracts the corresponding detection area in the processed circuit board image through model training, and the method comprises the following steps:
collecting a plurality of standardized circuit board images, manually marking corresponding detection areas in the circuit board images by adopting a rectangular frame according to different circuit board detection tasks, and establishing a training data set;
inputting the training data set into the detection region segmentation model for model training to obtain a trained detection region segmentation model;
and inputting the processed circuit board image into a detection area segmentation model, and marking the corresponding detection area in the processed circuit board image by the detection area segmentation model according to the detection task.
3. The visual inspection system for a power circuit board of claim 2, wherein: the image processing module performs image enhancement, image noise reduction and image extraction on the circuit board image, wherein
Image enhancement: selecting a gray level transformation area, modifying the gray level value of the image through linear operation, and carrying out gray level transformation on the gray level transformation area;
image denoising: selecting a noise reduction window by taking a target pixel as a center, calculating an average value of all pixels in the noise reduction window to replace an original pixel value, keeping the shape and the size of the window, and replacing each pixel in the image by the average value by taking the pixel as the center;
image extraction: and determining the image edge by detecting the position of the image gray value step change, and extracting the processed circuit board image according to the image edge.
4. The visual inspection system for a power circuit board of claim 2, wherein: the detection item selection module is used for selecting one or more detection tasks from screws, flat cables, inductors, fuses, electrolytic capacitors and PCB boards.
5. The visual inspection system for power circuit boards of claim 3 wherein: and the defect detection module carries out defect detection on the circuit board image marked with the detection area and the corresponding area in the standardized circuit board image through pixel-by-pixel comparison.
6. The visual inspection system for power circuit boards of claim 5 wherein: further comprising:
the detection area self-defining module is used for self-defining and marking a detection area in the standardized circuit board image;
the detection information statistic module is used for counting the defect detection result of the defect detection module;
and the detection result display module is used for displaying the related detection result of the circuit board detection task and the statistical result of the detection information statistical module.
7. The visual inspection system for power circuit boards of claim 5 wherein: the character recognition module carries out character recognition on the region to be recognized marked by the region to be recognized extraction model, and the character recognition method comprises the following steps:
acquiring a nameplate image marked with a region to be recognized, performing OCR recognition, and inputting OCR recognition results into a language model one by one to obtain an OCR output sequence set;
and converting the output sequences in the OCR output sequence set into digital vectors one by one, performing dimensionality reduction processing, and inputting the dimensionality reduced digital vectors into a recurrent neural network one by one to obtain a text sequence.
8. The visual inspection system for a power circuit board of claim 7, wherein: the model training is carried out to the standardized data plate image of waiting to discern the regional extraction model of waiting to discern through artifical mark, includes:
manually labeling areas to be identified, including areas where power supply types and power supply numbers are located, in the standardized nameplate images at different angles and under different illumination conditions, and establishing a training data set;
and inputting the training data set into the region to be recognized extraction model for model training to obtain the trained region to be recognized extraction model.
9. The visual inspection system for a power circuit board of claim 8, wherein: the mechanical arm control module is used for controlling the mechanical arm according to the defect detection result of the defect detection module and the comparison result of the data comparison module;
when the defect detection result of the defect detection module is lower than the detection result of the corresponding detection task of the good product, or the data comparison module compares and judges that the power model identified by the character identification module is inconsistent with the power model to be detected, the controller controls the mechanical arm to clamp the corresponding circuit board to be detected to the unqualified product through the mechanical arm control module.
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