Research on PCB defect detection algorithm based on improved  YOLOv4-tiny

Yushuai Fang; Haicheng Wang; Zhenlu Li; Wei Huang

doi:10.1117/12.3004737

20 October 2023 Research on PCB defect detection algorithm based on improved YOLOv4-tiny

Yushuai Fang, Haicheng Wang, Zhenlu Li, Wei Huang

Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129162L (2023) https://doi.org/10.1117/12.3004737
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China

Abstract

An improved YOLOV4-tiny algorithm was proposed to address the difficulties in detecting small targets and high complexity in Printed Circuit Board (PCB) defect detection, as well as the inability to meet real-time detection requirements. Firstly, the backbone network was changed to a lighter MobileNet-V3 to solve the problem of excessive parameter quantity, making the model more lightweight. Secondly, the detection scale was increased to three, enhancing the network models to detect small target defects. An improved SPP module was proposed to further improve the feature map expression ability. Finally, the anchor box sizes were re-clustered using the K-means algorithm to accelerate network convergence. It was learned through experiments that the accuracy of this algorithm improved by 4.28%, 1.03%, and 4.94% compared to SSD, YOLOv3, and YOLOv4-tiny algorithms, respectively. The model size was reduced by 1.4 MB compared to YOLOv4-tiny, and the detection speed reached 83.33FPS that satisfies the demands for real-time detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yushuai Fang, Haicheng Wang, Zhenlu Li, and Wei Huang "Research on PCB defect detection algorithm based on improved YOLOv4-tiny", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129162L (20 October 2023); https://doi.org/10.1117/12.3004737

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KEYWORDS

Detection and tracking algorithms

Defect detection

Small targets

Target detection

Feature extraction

Object detection

Education and training

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