Research on PCB defect detection method based on improved YOLOv8

Minghui Wang; Chao Yin; Zipeng Zhang

doi:10.1117/12.3035008

11 July 2024 Research on PCB defect detection method based on improved YOLOv8

Minghui Wang, Chao Yin, Zipeng Zhang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321038 (2024) https://doi.org/10.1117/12.3035008
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Reproduced circuit boards (PCBs) are a crucial element in contemporary electronic goods, and their manufacturing process presents challenges for environmental control and has stringent criteria. Defects such as holes, mouse bites, open circuits, short circuits, stabs, and miscellaneous copper resulting from this can lead to product quality issues and subsequently affect the performance of electronic devices using the product. Currently mainstream target detection frameworks still face problems with small object detection when it comes to PCB surface defects. The study introduces an enhanced YOLOv8 algorithm designed for the real-time identification of surface irregularities on PCBs. Specifically, this includes introducing attention mechanism in the neck structure of YOLOv8 to enhance focus on key features, and replacing PANet feature pyramid structure with BiFPN structure to better capture boundary information. According to the experimental results, the improved model has achieved significant effects in detecting small target defects. Compared to the YOLOv8 algorithm, the accuracy of the improved algorithm has been increased by 1.5% and reached 95.4%. it can accurately perform defect detection tasks for printed circuit boards in industrial production and greatly enhance the accuracy of detecting various types of defects, demonstrating the effectiveness of the algorithm.

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

Citation Download Citation

Minghui Wang, Chao Yin, and Zipeng Zhang "Research on PCB defect detection method based on improved YOLOv8", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321038 (11 July 2024); https://doi.org/10.1117/12.3035008

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KEYWORDS

Defect detection

Detection and tracking algorithms

Object detection

Deep learning

Image processing

Target detection

Image enhancement

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