Abstract:

Fabric defect detection plays an important role in controlling the quality of fabric surface. An effective fabric detection algorithm based on histogram of orientated gradient (HOG) and low-rank decomposition was proposed. Firstly, the test fabric image was divided into image block with the same size. A feature matrix was generated by extracting the HOG features of each block. Secondly, an efficient low-rank decomposition model was constructed, and alternating direction method(ADM) was adopted to decompose the feature matrix into a low-rank matrix and a sparse matrix. Finally, the saliency map generated by sparse matrix was segmented via an improved optimal threshold algorithm to locate the defect. The experiment results show that the proposed method can sufficiently improve the  defect ddetection performance of complicated textile texture patterns.

Key words: histogram of oriented gradient, low-rank decomposition, fabric image, defect detection