4.6 Article

YOLO-MBBi: PCB Surface Defect Detection Method Based on Enhanced YOLOv5

期刊

ELECTRONICS
卷 12, 期 13, 页码 -

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MDPI
DOI: 10.3390/electronics12132821

关键词

printed circuit board (PCB); defect detection; deep learning; YOLOv5

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An enhanced YOLO-MBBi network is proposed to detect surface defects on printed circuit boards (PCBs) and address the shortcomings of existing methods. Experimental results show that YOLO-MBBi outperforms YOLOv5s in terms of accuracy and real-time performance, achieving higher mAP50 and recall values while requiring fewer FLOPs and achieving a higher FPS value. The metrics also showed satisfactory accuracy when tested with another dataset, meeting the needs of industrial production.
Printed circuit boards (PCBs) are extensively used to assemble electronic equipment. Currently, PCBs are an integral part of almost all electronic products. However, various surface defects can still occur during mass production. An enhanced YOLOv5s network named YOLO-MBBi is proposed to detect surface defects on PCBs to address the shortcomings of the existing PCB surface defect detection methods, such as their low accuracy and poor real-time performance. YOLO-MBBi uses MBConv (mobile inverted residual bottleneck block) modules, CBAM attention, BiFPN, and depth-wise convolutions to substitute layers in the YOLOv5s network and replace the CIoU loss function with the SIoU loss function during training. Two publicly available datasets were selected for this experiment. The experimental results showed that the mAP50 and recall values of YOLO-MBBi were 95.3% and 94.6%, which were 3.6% and 2.6% higher than those of YOLOv5s, respectively, and the FLOPs were 12.8, which was much smaller than YOLOv7's 103.2. The FPS value reached 48.9. Additionally, after using another dataset, the YOLO-MBBi metrics also achieved satisfactory accuracy and met the needs of industrial production.

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