Xinyu Hu, Defeng Kong*, Xiyang Liu, Junwei Zhang, Daode Zhang
CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 915-933, 2024, DOI:10.32604/cmc.2023.046376
Abstract Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention network; then the improved K-means-SENet… More >
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