PCB Defect Detection and Enhancement System Based on MATLAB
Author: 
Ao GuoAuthors Info & Claims
Pages 261 - 269
Abstract
In the process of PCB manufacturing, with the increasing demand, the complexity of IC chips, the number of pins, and the density are getting higher and higher. Therefore, the rationality of PCB layout is particularly important. In many links of PCB production and processing, the quality of solder joints needs to be tested. In recent years, in order to improve the automation and efficiency of PCB solder joint quality inspection, the automatic optical inspection system of PCB solder joint based on CCD camera image has received more and more attention. In this paper, a PCB defect detection and enhancement system based on MATLAB is studied aiming at the problem of PCB solder joint location and defect detection. The main tasks of the system include PCB surface feature image acquisition, image enhancement and segmentation preprocessing, PCB surface feature extraction, neural network model establishment and defect identification, etc., completing PCB defect location and establishing PCB defect data set, in which BP neural network is used as the judgment model of this project, It can meet the current PCB surface defect monitoring requirements.
References
[1]
Chen Donghai, Li Peng, Zhou Yang, Wu Yuhao, Zhang Haiyong. Automatic signal point-to-point system of power dispatching master station system based on image recognition technology [J]. Electronic design engineering, 2022,30 (13): 73-77.
[2]
Hua Li, Kong Yaoyao, Chen Yongyi, Zhai Yalei. Research on fault diagnosis method of track structure based on GA-BP neural network [J]. Intelligent computer and application, 2022,12 (07): 123-128
[3]
The balance is correct Research on plant leaf image recognition based on improved depth residual network and attention mechanism [D]. Guangdong Normal University of technology, 2022
[4]
Liang Huan, Zhang Kai, Wang Ruiyuan, Yang Zihan, Chen Keqi. Accurate detection method of oil drilling leakage layer based on image recognition [J]. China Petroleum and chemical standards and quality, 2022,42 (12): 43-44 + 47
[5]
Lin Qiaozhi. Design and implementation of artificial intelligence slab information recognition management system based on image recognition technology [J]. Digital technology and application, 2022,40 (06): 176-178.
[6]
Su Jia, Jia Xinyu, Hou Weimin. PCB defect detection algorithm based on yolo-j [J / OL]. Computer integrated manufacturing system: 1-20 [2022-08-09] http://kns.cnki.net/kcms/detail/11.5946.tp.20220622.1623.005.html
[7]
Fan Xilin. A belt deviation detection method based on BP neural network [J]. Energy technology and management, 2022,47 (03): 116-118
[8]
Chen Jiahuan, Zeng Yun, Deng Yulin, Li Xiang, Qian Jing. Pressure pulsation prediction method of BP neural network optimized by mea [J]. Software guide, 2022,21 (06): 31-35
[9]
Ni Tianyu Research on fine-grained image recognition based on convolutional neural network optimization [D]. Northeast Electric Power University, 2022.
[10]
Guo Songlin, Ba Yankun, Li Chun. Improved SSA algorithm to optimize BP neural network power load forecasting model [J]. Journal of Heilongjiang University of science and technology, 2022,32 (03): 401-405
[11]
Chen long Research on rapid positioning and detection method of PCB solder joints based on linear CCD [D]. University of Electronic Science and technology, 2022.
[12]
Sun Zhichao, Wang Bo, Zhang Xiaoling. PCB defect detection based on deformable residual convolution and scalable feature pyramid algorithm [J / OL]. Telecommunication technology: 1-9 [2022-08-09] http://kns.cnki.net/kcms/detail/51.1267.tn.20220427.1608.012.html
[13]
Zhuan sun has great ambitions Surface defect detection system of PCB bare board based on image processing [D]. University of Electronic Science and technology, 2022.
[14]
Li Xiaomin, Chen Zhu, Li Heng, Bai Yirui, Fang Hui. Design of control system for PCB substrate glass surface defect detection equipment [J]. Modern manufacturing engineering, 2022 (02): 124-128.
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Published In
November 2022
739 pages
ISBN:9781450396806
DOI:10.1145/3582935
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 April 2023
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Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICITEE 2022
ICITEE 2022: 5th International Conference on Information Technologies and Electrical Engineering
November 4 - 6, 2022
Changsha, China
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