| Summary: | The detection of defects is indispensable in industrial production. Surface defects have different scales. Both minimal flaws and significant scratches may appear on the same product. The standard method uses a multi-scale feature fusion network, introducing many parameters that may reduce the inference speed. In actual industrial production scenarios, inference speed and accuracy play an equally important role. Therefore we propose an algorithm to effectively improve the detection speed while improving the detection accuracy. The model proposed in this paper called “YOLO with lightweight feature fusion network (LFF-YOLO).” First, we use ShuffleNetv2 as a feature extraction network to reduce the number of parameters. Then, to improve the efficiency of multi-scale feature fusion, we propose the lightweight feature pyramid network (LFPN). Considering that the fixed receptive field is difficult to adapt to the defects of different scales, it may lead to the difficulty of model convergence and seriously affect the detection performance. Therefore, we propose the adaptive receptive field feature extraction (ARFFE) module, which weights the multi-receptive field channels to generate multi-receptive field information. In addition, focal loss is used to solve the problem of imbalance between positive and negative samples. Finally, we conducted experiments on NEU-DET (79.23% mAP), Peking University printed circuit board defect dataset (93.31% mAP),and GC10-DET (59.78% mAP), respectively. Extensive experiments show that our proposed method achieves optimal detection speed compared with the prevailing methods, and the detection accuracy of our method is also highly competitive. We open-soure our code in the following URL:<uri>https://github.com/syyang2022/LFF-YOLO</uri>
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