GCC-YOLOv7: accelerate printed circuit board small target detection

Jianwei Liang; Xuchang Yuan; Hui Li

doi:10.1117/12.3026101

8 April 2024 GCC-YOLOv7: accelerate printed circuit board small target detection

Jianwei Liang, Xuchang Yuan, Hui Li

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130902D (2024) https://doi.org/10.1117/12.3026101
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Given the low detection accuracy, slow speed, and impoverished comprehensiveness of existing Printed Circuit Board (PCB) surface defect detection in small target detection, a lightweight YOLOv7 improved algorithm is proposed. First, the ordinary convolution operation is replaced by Ghost, the model parameters are significantly reduced, the accuracy is almost no loss, and then the Convolutional Block Attention Module (CBAM) is added. Finally, the Contextual Transformer module is added to the detection header to increase the model’s focus on defect features, which can combine contextual feature information to improve the representation of small target features. Ablation experiments were performed on the PCB datasets provided by the Intelligent Robot Open Laboratory of Peking University, combined with the datasets of 10,000 different PCB defect images collected by ourselves. The experimental results showed that the enhanced model reduced the number of parameters by 54% compared with the previous model, and the detection precision mAP was 97.02%. The running speed is 53.4 FPS, which suits the need for high detection precision and speed in industrial.

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

Citation Download Citation

Jianwei Liang, Xuchang Yuan, and Hui Li "GCC-YOLOv7: accelerate printed circuit board small target detection", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130902D (8 April 2024); https://doi.org/10.1117/12.3026101

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KEYWORDS

Object detection

Convolution

Defect detection

Detection and tracking algorithms

Target detection

Small targets

Commercial off the shelf technology

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