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PCB defect automatic detection method based on deep learning

An automatic detection and deep learning technology, applied in image data processing, instruments, character and pattern recognition, etc., can solve the problems of low efficiency and low precision, achieve high fault detection rate, save labor costs, improve reliability and efficiency sexual effect

Pending Publication Date: 2020-10-30
716TH RES INST OF CHINA SHIPBUILDING INDAL CORP
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for automatic detection of PCB defects based on deep learning, which can solve the problems of low efficiency and low precision in PCB defect detection, realize automatic detection and target recognition of six major defects, and save labor costs at the same time. Greatly improve the efficiency and reliability of defect detection

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  • PCB defect automatic detection method based on deep learning
  • PCB defect automatic detection method based on deep learning

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Embodiment

[0032] Such as figure 1 As shown, a method for automatic detection of PCB faults based on deep learning, the steps are as follows:

[0033] (1) Preprocess the original data, convert the data format and size, and complete the data set division;

[0034] Firstly, the PCB image is cropped into 600×600 sub-images, and then the data set format is converted into a unified data input format for the TensorFlow interface, and then the data set is divided into two parts: training and inference. Among them, the training data set contains 8250 images, and the test data set contains 2102 images.

[0035] (2) Carry out data enhancement processing on the preprocessed image;

[0036] Purpose of data augmentation:

[0037] 1) Expand the training samples to reduce the risk of over-fitting of the parameters of the neural network;

[0038] 2) Reduce the cost of collection, the number of PCBs with defects is small, and the collection cycle is long;

[0039] The present invention adopts six tr...

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Abstract

The invention discloses a PCB defect automatic detection method based on deep learning, and the method comprises the following steps: (1) carrying out the preprocessing of original data, carrying outthe data format and size conversion, and completing the data set division; (2) performing data enhancement processing on the preprocessed image; (3) inputting the enhanced data into a new micro defectdetection network, and training a defect detection model; (4) performing performance evaluation on the model obtained after training; and (5) further optimizing the model by referring to an evaluation result in the step (4). According to the invention, automatic detection and target identification of PCB defects are realized, the problems of low efficiency and low precision during PCB defect detection are solved, and the efficiency and reliability of defect detection are greatly improved while the labor cost is saved; and the automatic defect detection method provided by the invention is easyto expand to micro defect detection in other fields, such as fabric defect detection and metal surface defect detection.

Description

technical field [0001] The invention belongs to the field of printed board defect detection and small target recognition, and in particular relates to an automatic detection method for PCB defects based on deep learning. Background technique [0002] A printed circuit board (PCB) is essentially a board of electronic components that are mechanically supported and electrically connected. It is a fundamental part of the design process of all electronics and has grown over the years into a very complex component. PCBs are widely used in all but the simplest electronic products. According to the 2019 global single-sided printed circuit board market analysis report, the printed circuit board market will reach $85 billion by 2025. However, currently, visual defect detection to ensure PCB product quality is usually the largest cost of PCB manufacturing. In recent years, the automatic optical inspection (AOI) system has replaced most of the manual inspection and improved the detect...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/136G06T7/60G06K9/62
CPCG06T7/0004G06T7/11G06T7/136G06T7/60G06T2207/20016G06T2207/20081G06T2207/20132G06T2207/30141G06F18/23213
Inventor 杨鸿斌徐国强杨建方新茂路朗李超祁徳昊马若飞丁进徐炜
Owner 716TH RES INST OF CHINA SHIPBUILDING INDAL CORP
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