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Fabric defect detection algorithm based on improved YOLOv5

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Abstract

Fabric defect detection is an important part of the textile industry, aiming at the problems of many types of fabric defects, small size defects and unbalanced samples, an improved YOLOv5 fabric defect detection algorithm, FD-YOLOv5, was proposed. First, the coordinate attention module is embedded in the YOLOv5 backbone network structure to replace the bottleneck structure in the original network model. While reducing the amount of parameters and calculation, it enhances the ability of the network to extract features and improves the model's ability to detect small target defects. Secondly, a smoother Mish activation function is used in the original model convolution structure for model training, which improves the nonlinear expression ability of the model; the SIoU loss function considering the direction of the anchor box is used to improve the convergence speed and detection accuracy of the model. Finally, combining the focal loss and GHM loss functions as the target confidence loss function to solve the problem of sample imbalance in the fabric defect dataset. The experimental results based on the public fabric defect dataset of Aliyun TianChi shows that the mAP@.5 and mAP@.5:.95 of the improved algorithm are 65.1% and 30.4%, respectively, which are 8.3% and 3.2% higher than the original model, respectively, and the parameter amount, calculation amount and weight of the model are reduced by 8.4%, 11.2% and 14.3%, respectively, compared with the original model. Even compared with the state-of-the-art YOLOv7 model, the mAP@.5 value of the proposed model is improved by 6.5%. Although the FPS value is lower than YOLOv7 model, it also achieves a detection speed of 79 frames per second, which can meet the real-time demand. The experimental results demonstrate the effectiveness of the method in this paper, which can provide a reference for the automatic detection method of fabric defects.

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Data availability

The datasets generated during the current study are available in the TianChi website of Aliyun [https://tianchi.aliyun.com/dataset/79336].

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Funding

This paper was funded and technically supported by the “National Innovation Center of Advanced Dyeing & Finishing Technology”.

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Authors and Affiliations

  1. School of Computer Science and Technology, Donghua University, Shanghai, 201600, China

    Feng Li & Kang Xiao

  2. National Innovation Center of Advanced Dyeing & Finishing Technology, Tai’an, 271000, Shandong, People’s Republic of China

    Feng Li, Zhengpeng Hu & Guozheng Zhang

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  1. Feng Li
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  2. Kang Xiao
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  3. Zhengpeng Hu
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  4. Guozheng Zhang
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Contributions

All authors contributed to the study deign. Data collection and analysis were performed by KX and FL. The draft of the manuscript was written by KX, and FL commented on previous versions of the manuscript. Experimental guidance and equipment were provided by ZH and GZ. All authors read and approved the final manuscript.

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Correspondence to Kang Xiao.

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Li, F., Xiao, K., Hu, Z. et al. Fabric defect detection algorithm based on improved YOLOv5. Vis Comput 40, 2309–2324 (2024). https://doi.org/10.1007/s00371-023-02918-7

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