Compact spatial arrangement of multiple sparse filters for fabric defect detection

Abstract

Fabric defect detection is one of the hot topics in the textile industry, primarily due to occasional machine failures and repetitive errors on production lines leading to misalignment in warp and weft. Consequently, collecting defect samples is challenging, as these defects appear as subtle, linear shapes in horizontal or vertical orientations, resulting in sparse defect features with low contrast. This poses significant challenges to the model’s feature learning capabilities and detection performance. we developed a research platform for developing an automatic fabric detection system, focusing on collecting fabric defect images and developing/deploying detection algorithms. Utilizing this platform, a large number of fabric images were collected and annotated on the denim production line in textile enterprises, with the dataset being made publicly available for research within the field. Mainstream methods such as local attention or multiscale fusion have limited effectiveness in detecting defects in denim, which exhibit sparse features, varied morphologies, low contrast, and a lack of global features. This paper designs multiple sparse filters for feature extraction. Square filters are utilized to extract minute defect features in fabric images, while asymmetric filters are employed to capture linear defect characteristics, enhancing the model’s representation capabilities. Furthermore, a compact arrangement involving multiple sparse filters is proposed to enhance feature diversity in model learning and reduce the risk of overfitting. Experiments conducted on the dataset provided in this paper and the TIANCHI dataset demonstrate that the method proposed in this paper outperforms existing mainstream algorithms. The denim fabric dataset can be obtained on GitHub: https://github.com/h-z-liang/DEMIN_DATASET.git.

Keywords

Fabric defect detection sparse filter fabric dataset convolutional neural network

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