Fabric Defect Detection Based on Biological Vision Modeling

Abstract

Fabric defect detection plays a key role in the quality control of textiles. Existing fabric defect detection methods adopt traditional pattern recognition methods; however, these methods lack adaptability and present poor detection performance. Because biological vision system has the ability to quickly locate salient objects, we propose a novel fabric defect detection algorithm based on biological vision modeling by simulating the mechanism of biological visual perception. First, a distinct, efficient, and robust feature descriptor from the biological modeling of P ganglion cells, which was proposed in our previous work, is adopted to improve the representation of fabric images with complex textures. To account for the low-rank and sparsity characteristics of biological vision, the low-rank representation (LRR) technique is adopted to model biological visual saliency, and it can decompose the fabric image into backgrounds and salient defect objects. Meanwhile, dictionary learning and Laplacian regularization are integrated into the LRR model as follows: 1) dictionary learning is used to denoise the saliency map; and 2) Laplacian regularization enlarges the gaps between defective regions and the background. Finally, the linearized accelerated direction method with adaptive penalty is adopted to solve the proposed model. The experimental results emphasize that the proposed algorithm has good detection performance for plain or twill fabrics with simple textures as well as for patterned fabrics with complex textures. Moreover, the proposed method is superior to the state-of-the-art methods in terms of its adaptability and detection efficiency.