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.

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The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various scales in RSIs of complex scenes is challenging, and (2) feature reconstruction for accurate segmentation is difficult. To solve these problems, we propose a deep-separation-guided progressive reconstruction network that achieves accurate RSI segmentation. First, we design a decoder comprising progressive reconstruction blocks capturing detailed features at various resolutions through multi-scale features obtained from various receptive fields to preserve accuracy during reconstruction. Subsequently, we propose a deep separation module that distinguishes various classes based on semantic features to use deep features to detect objects of different scales. Moreover, adjacent middle features are complemented during decoding to improve the segmentation performance. Extensive experimental results on two optical RSI datasets show that the proposed network outperforms 11 state-of-the-art methods.