Abstract
Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of varying scales, irregular shapes and many small objects, an improved YOLOv4 object detection algorithm for fabric defects is proposed. Firstly, in order to improve the detection accuracy of small objects, the RFB module is introduced and fused with shallow features, which can obtain receptive fields of different scales to improve the features extracted from the backbone network. Secondly, the introduction of spatial and channel attention mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv4 object detection algorithm in fabric defect map detection is 71.89%. The improved algorithm can accurately improve the accuracy of fabric defect positioning.
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Wang, Y., Hao, Z., Zuo, F., Su, Z. (2021). Fabric Defect Target Detection Algorithm Based on YOLOv4 Improvement. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_56
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DOI: https://doi.org/10.1007/978-3-030-87571-8_56
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