YAO Ming-hai,GU Qin-long.A sparse representation method for image-based surface defect detection[J].Optoelectronics Letters,2018,14(6):476-480
A sparse representation method for image-based surface defect detection
Author NameAffiliation
YAO Ming-hai College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, Chin 
GU Qin-long College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, Chin 
Abstract:
      In this paper, an efficient sparse representation-based method is presented for detecting surface defects. The proposed method uses the sparse degree of coefficient in the redundant dictionary for checking whether the test image is defective or not, and the binary representation of the defective images is obtained, according to the global coefficient feature. Owing to the requirements for the efficiency and detecting quality, the block proximal gradient operator is introduced to speed up the online dictionary learning. Considering the correlation among the testing samples, prior knowledge is applied in the orthogonal-matching-pursuit sparse representation algorithm to improve the speed of sparse coding. Experimental results demonstrate that the proposed detection method can effectively detect and extract the defects of the surface images, and has broad applicability.
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