International Journal of Frontiers in Engineering Technology, 2022, 4(10); doi: 10.25236/IJFET.2022.041005.
Jianfei Yuan, Yongkang Peng
Jingdezhen Ceramic University, Jingdezhen, Jiangxi, 333403, China
Stimulated by the increasing demand for electronic equipment, the global PCB market is expanding rapidly, and the detection of PCB defects is becoming more and more important. However, the small target, dense and other problems that are difficult to detect pose a great challenge to the accuracy and real-time performance of PCB defect detection. In this paper, YOLOv5 algorithm is applied to the field of PCB defect detection, and the corresponding YOLOv5-SlimNeck lightweight network is adopted. The experiment shows that YOLOv5l6 can detect all kinds of small and dense defects of PCB, with mAP.5 reaching 95.4%. On the other hand, YOLOv5-SlimNeck reduced the parameters by 89.6% and mAP.5 reached 94%. Compared with the lightweight network yolov5s6 in the official library, YOLOv5s6, YOLOv5-SlimNeck has only 64.5% parameters, and mAP.5 is 2.4% higher than YOLOv5s6.
PCB; defect detection; YOLO
Jianfei Yuan, Yongkang Peng. Defect Detection Method of PCB Based on Improved YOLOv5. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 10: 28-33. https://doi.org/10.25236/IJFET.2022.041005.
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