Surface defect detection for intricate pattern fabrics based on deep learning

To tackle the challenges in the current detection of patterned fabric defects, such as unbalanced and limited data volume, incomplete extraction of defect features by the network, aliasing effect in feature fusion by the pyramid network, and low accuracy in identifying small targets, this paper proposes a novel single-stage network named ASC-you only look once (YOLO). Firstly, the CoT-CSP module is introduced into the Backbone network of the YOLOv5s to model the global contextual relationships of defective features. This module compensates for the limitations of the original Cross-Stage Partial Network, which can only extract local feature information. It effectively enhances the comprehensive extraction of various irregular defect features in fabrics and reduces redundant interactions with texture background noise. Subsequently, three types of AttentionC3 modules are proposed and integrated into the Neck enables the model to focus on valuable details related to defect features through weight allocation, significantly enhancing the capability to identify small defects. Finally, an adaptive spatial feature fusion network is designed to optimize the pyramid network by redistributing weights across different feature layers for efficient utilization of multi-level feature information. Additionally, a random erasing approach is proposed to individually enhance the defect images of specific types with a limited number and adjust the corresponding labels simultaneously. This process aims to improve the robustness of the ASC-YOLO network against data noise. Experimental results demonstrate that ASC-YOLO achieves 95.8% accuracy, with mean average precision@0.5 reaching 83.1%. Meanwhile, the experiments also verify its superiority over other fabric detection methods and its ability to effectively improve the accuracy of fabric defect detection.