Abstract
Fabric defect detection is a crucial step for the fabric production process. However, existing traditional methods of fabric defect detection only pay attention to the simplest plain and twill fabrics, while ignoring the complicated diverse texture fabric. In this paper, we proposed a robust defect detection method based on CenterNet for diverse texture fabric. Firstly, we used ResNet-50 backbone network to extract the fabric image features. Then we applied up-sampling of three-fold deconvolution to obtain the high-resolution feature images. Finally, defects in the diverse texture were located and recognized. Experimental results showed that the proposed method can successfully detect the defects and achieve a high mAP. Compared with the current state-of-the-art methods, such as Faster-RCNN, EffcientDet and Yolo-V4, the proposed method outperforms these methods in terms of accuracy and robustness.
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Acknowledgment
This work was supported by financial support received from the National Natural Science Foundation of China (No. 61902302). Shaanxi Provincial Key R&D Program Project (2021GY-261), Shaanxi Innovation Ability Support Program (2021TD-29).
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Kong, W., Zhang, H., Jing, J., Shi, M. (2021). A Defect Detection Method for Diverse Texture Fabric Based on CenterNet. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_55
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