Fabric Defect Detection Based on Hybrid Attention Transformer and Improved Cascade R-CNN

Various defects arise during textile production, making fabric defect inspection essential for production and quality management in the textile industry. However, fabric defect detection techniques face challenges due to defects with disparate aspect ratios, high foreground-background similarity, and tiny sizes. Based on these issues, we propose a fabric defect detection method that combines an improved Cascade R-CNN (SPCNet) network and Super-Resolution reconstruction technology. Firstly, defective images are reconstructed using a hybrid attention Transformer (HAT), enhancing texture details and edge information. We design a new multi-stage defect detection model SPCNet to identify fabric defects. The architecture includes a feature extraction module based on Switchable Atrous Convolution (SAC). SAC can obtain a larger receptive field for better detection of tiny defects. Path Aggregation Network (PANet) is introduced to improve the recognition of scale-unbalanced defects. In addition, Cascade RPN (C-RPN) is adopted to fully use deep and shallow features. To solve the issue of imbalanced defect classes, we adopt Class-aware Sampling (CAS) strategy. Soft-Nmsis used to reduce the false deletion of defective feature detection boxes. Comparative experimental results demonstrate that the defect detection method combining HAT and SPCNet can significantly raise the overall recognition rate of multiclass fabric defects, exceeding the performance of other current methods.