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- W3201217929 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." @default.
- W3201217929 created "2021-09-27" @default.
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- W3201217929 date "2021-01-01" @default.
- W3201217929 modified "2023-09-26" @default.
- W3201217929 title "Fabric Defect Target Detection Algorithm Based on YOLOv4 Improvement" @default.
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- W3201217929 doi "https://doi.org/10.1007/978-3-030-87571-8_56" @default.
- W3201217929 hasPublicationYear "2021" @default.
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