Research on PCB board surface defect detection algorithm model based on deep learning

Qian Wu; Lin Li; Zhiyong Lu; Jiangbo Liu; Qinhao Kang

doi:10.1117/12.3035113

19 July 2024 Research on PCB board surface defect detection algorithm model based on deep learning

Qian Wu, Lin Li, Zhiyong Lu, Jiangbo Liu, Qinhao Kang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132A (2024) https://doi.org/10.1117/12.3035113
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In order to solve the puzzles such as missed detection, poor real-time performance, low accuracy, and limited front-end hardware in PCB board surface defect detection, an improved lightweight model based on YOLOv5s is proposed. First, the C3Ghost module is used to replace the C3 module in the model backbone network, secondly the lightweight GhostConv convolution compression model is introduced, Once again ECANet is added to the backbone network to strengthen the ability to extract key information, and finally the image data set is re-clustered with the help of K-means clustering + genetic algorithm. Experimental results show the Parameters and Size of the model proposed in this article are reduced by 32.3% and 33.8% respectively, reducing the dependence on the detection front-end hardware conditions and improving the detection performance mAP@0.5 of the model. Its detection accuracy is high, the research shows that using the improved model to identify and classify PCB surface defect detection has better real-time performance and higher detection accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qian Wu, Lin Li, Zhiyong Lu, Jiangbo Liu, and Qinhao Kang "Research on PCB board surface defect detection algorithm model based on deep learning", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132A (19 July 2024); https://doi.org/10.1117/12.3035113

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available