Abstract
Real-time detection of wire surface defects is an important part of wire quality detection. Because traditional algorithms need lots of parameters and have weak universality, besides, time performance of detector based on candidate region is poor. To solve these problems, we study the effectiveness of single-stage detector in real-time detection, and propose a detection algorithm of wire surface defects combining enhanced darknet53 and feature pyramid (FPN). Firstly, we use CBAM_Darknet53 which introduces channel attention and spatial attention to extract more differentiated features. Secondly, considering the large change of defects, we use the multi-level feature pyramid (MLFPN) which adds the maximum pooling layer to fuse multi-level features to detect multi-scale defects. Then we reprocess the detector to improve the detection rate of defects and the accuracy of the detection box. Finally, network structure is optimized by modifying loss function. Experiments on defect datasets in real industrial environments show that recall and mAP of this method reach 94.49% and 88.46%, which is higher than state-of-the-art methods.
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Yi, X., Song, Y., Zhang, Y. (2020). Enhanced Darknet53 Combine MLFPN Based Real-Time Defect Detection in Steel Surface. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_25
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DOI: https://doi.org/10.1007/978-3-030-60633-6_25
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