Fabric Defect Detection Algorithm Based on Multi-channel Feature Extraction and Joint Low-Rank Decomposition

Fabric defect detection plays an important role in the quality control of fabric products. In order to effectively detect defects for fabric images with numerous kinds of defects and complex texture, a novel fabric defect detection algorithm based on multi-channel feature extraction and joint low-rank decomposition is proposed. First, at the feature extraction stage, a multi-channel robust feature (Multi-channel Distinctive Efficient Robust Feature, McDerf) is extracted by simulating the biological visual perception mechanism for multiple gradient orientation maps. Second, joint low-rank decomposition algorithm is adopted to decompose the feature matrix into a low rank matrix and a sparse matrix. Finally, for the purpose of localizing the defect region, the threshold segmentation algorithm is utilized to segment the saliency map generated by sparse matrix. Comparing with the existing fabric defect detection algorithms, the experimental results show that the proposed algorithm has better adaptability and detection efficiency for the plain and patterned fabric images.