Abstract
Printed Circuit Board (PCB) surface defect detection is a crucial part of the PCB production and manufacturing process, which is vital in the manufacturing of electronic devices, and it is a challenging task to realize efficient and accurate PCB detection. In this paper, we propose an improved PCB surface defect detection algorithm YOLO-VanNet based on YOLOv5, whose network structure mainly includes: using K-Means++ to improve the original K-Means algorithm to generate anchors more in line with the labeling information; Scale the anchors within a certain range by the labeling information of the dataset to make them more suitable for the current dataset and speed up the training speed of the model; Using VanillaNet to replace the original backbone network in the backbone network to strengthen the performance of the network, reduce the number of layers of the network while reducing the redundant computation, reduce the requirements of the model on the hardware resources, and achieve a more efficient feature extraction. Extensive experiments on the PKU-Market-PCB dataset show that the YOLO-VanNet model achieves 90.6% mAP (Mean Average Precision) and 95.1% precision, which provides faster training speed and better detection performance compared to the YOLOv5 model.
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Chen, F., Shi, C., Zhu, D., Zhou, C. (2024). YOLO-VanNet: An Improved YOLOv5 Method for PCB Surface Defect Detection. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14961. Springer, Singapore. https://doi.org/10.1007/978-981-97-7232-2_30
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DOI: https://doi.org/10.1007/978-981-97-7232-2_30
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