Fabric defect detection based on feature enhancement and complementary neighboring information

Fabric defect detection is a crucial aspect of quality control in the textile industry. Given the complexities of fabric backgrounds, the high similarity between patterned backgrounds and defects, and the variety of defect scales, we propose a fabric defect detection method based on feature enhancement and complementary neighboring information. The core of this method lies in two main components: the feature enhancement module and the neighboring information complementation strategy. The feature enhancement module includes two sub-modules: similarity feature enhancement (SFE) and edge detail feature enhancement (EDFE). The SFE aims to capture the similarities between features to strengthen the distinction between defects and complex backgrounds, thereby highlighting the correlations among defects and the differences between defects and the background. The EDFE focuses on improving the network's ability to capture the edge details of fabrics, preventing edge information from becoming blurred or lost due to deeper network layers. The neighboring information complementation strategy consists of shallow-level information complementation (SLIC) and top-down information fusion complementation (TDIFC). The SLIC integrates newly introduced shallow features with neighboring features that have a smaller semantic gap, injecting richer detail information into the network. The TDIFC adaptively guides the interaction of information between adjacent feature maps, effectively aggregating multi-scale features to ensure information complementarity between features of different scales. Additionally, to further optimize model performance, we introduced partial convolution (Pconv) in the backbone of the feature extraction network. Pconv reduces redundant computations and decreases the model's parameter count. Experimental results show that our proposed method achieved an mAP@50 of 82.4%, which is a 6.6% improvement over the baseline model YOLOv8s. The average inference frame rate reached 61.8 FPS, meeting the real-time detection requirements for fabric defects. Moreover, the model demonstrated good generalization capabilities, effectively adapting to detecting defects in different types and colors of fabrics.