Deformation convolution and self-attention for fabric defect detection

Xiangcong Lv; Hui Li; Beibei Shen; Yifan Wu

doi:10.1117/12.2644656

12 October 2022 Deformation convolution and self-attention for fabric defect detection

Xiangcong Lv, Hui Li, Beibei Shen, Yifan Wu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420J (2022) https://doi.org/10.1117/12.2644656
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Accurate and fast detection of fabric defects is of great significance to improve the production efficiency of textile enterprises. However, fabric defects have problems such as large-scale changes, small objects, and unbalanced numbers. Therefore, a fabric detection method integrating deformation convolution and self-attention is proposed. The algorithm effectively alleviates the problem of the model's insufficient ability to extract irregular flaw features by combining multiscale feature extraction with deformation convolution; At the same time, combined with the self-attention mechanism, a dual-channel feature fusion is designed, and adaptive adjustment and fusion are performed to obtain more effective features to make up for the low detection rate of small object defects. Finally, an adaptive bounding box generator is designed in the region proposal network to obtain more accurate object bounding boxes for subsequent detection and regression. Experimental results show that the proposed method has a good detection effect, and effectively improves the accuracy and efficiency of fabric defect defection.

Citation Download Citation

Xiangcong Lv, Hui Li, Beibei Shen, and Yifan Wu "Deformation convolution and self-attention for fabric defect detection", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420J (12 October 2022); https://doi.org/10.1117/12.2644656

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