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Small-scale block defect detection of fabric surface based on SCG-NET

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Abstract

In contrast to the common small target detection problems, it is more difficult to locate and identify the small surface defects of fabric due to its own texture and complex background interference. Therefore, this paper proposes an effective detector for small-scale block defects on fabric surface by taking advantage of the backbone which integrates the Coordinate Attention module to enhance the acquisition of small-scale block defect location information. The FPN + PAN multi-scale detection structure is adopted to effectively integrate the feature information between different levels and deal with the multi-scale problem of defects. In the Neck section, a small target detection layer is set to expand the receptive field to prevent the loss of small-scale defect feature information. Moreover, we propose to use the GhostBottleneck module instead of the ordinary downsampling process to eliminate redundant convolutional calculations to improve the detection speed. The experimental results show that the optimal detection results of 0.56 and 0.842 are achieved in the detection recall and accuracy of the public fabric dataset; compared with other detectors, the result of small-scale defect detection rate is reduced by at least 2.7%, and the detection process meets the real-time requirement of automatic defect detection, which verifies the effectiveness of our method. Code and data are available at: https://github.com/VIMLab-hfut/SCG-NET.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (201904d07020010) and the Scientific and Technological Achievement Cultivation Project of Intelligent Manufacturing Research Institute of Hefei University of Technology (IMIPY2021022).

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  1. Hefei University of Technology, Hefei, China

    Mei Chen, Fan Jin, Qiang Lu, Quanhao Yu, Wei Chen & Xin Li

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  1. Mei Chen
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Correspondence to Xin Li.

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Mei Che, Fan Jin, Qiang lu, Quanhao Yu, Wei Chen, and Xin Li declare that they have no conflict of interest or financial conflicts to disclose. The authors declare that they have no conflict of interest.

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Chen, M., Jin, F., Lu, Q. et al. Small-scale block defect detection of fabric surface based on SCG-NET. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03289-3

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