Weft-knitted fabric defect classification based on a Swin transformer deformable convolutional network

With the vigorousness of the knitting industry, defect detection and classification of weft-knitted fabrics have become the research fields with extensive application value. However, convolution neural network models suffer from the limitation of convolutional operation, which makes them unable to capture the global features of fabric images abundantly. Although the transformer can compensate for this deficiency, it still has shortcomings such as poor small target recognition and unsatisfactory local information extraction ability. In order sufficiently to actualize the mutual support of relative advantages between the convolution neural network and the transformer, a Swin transformer deformable convolutional network integrated model is proposed in this paper. The Swin transformer deformable convolutional network utilizes the self-attention mechanism with global perception to establish dependencies comprehensively between long-range elements. Meanwhile, the deformable convolution is introduced according to the shaped characteristics of defects to extract local features effectively. Furthermore, a dataset containing 5474 images of weft-knitted fabrics was designed due to the less adequate databases. Experimental results on our weft-knitted fabric dataset and the Irish Longitudinal Study on Ageing (TILDA) database demonstrated that the proposed Swin transformer deformable convolutional network is superior to current state-of-the-art methods and has immense potential in fabric defect detection and classification.