Fabric Defect Detection Based on Local Optimum Analysis
LIU Wei1, CHANG Xingzhi2, LIANG Jiuzhen1, JIA Liang1, GU Chengxi1
1.School of Information Science and Engineering, Changzhou University, Changzhou 213164
2.Open Laboratory of Smart Manufacturing and Industrial Cloud Computing, Changzhou College of Information Technology, Changzhou 213164
Aiming at the detection of textile defects with complex periodic patterns, an unsupervised fabric defect detection method based on modified Markov random field model is proposed. The defects of periodic textile images are detected and the areas of defect are judged via the Markov neighborhood feature. The minimum image block computing unit of Markov random field is determined by combining the segmentation of periodic image, and the computational complexity of the algorithm is reduced. In the definition of the random field potential function, the difference of adjacent image blocks is comprehensively taken into account. The location of defect area is judged by the global characteristics of the Markov random field. The concept of fuzzy similarity relation matrix is introduced to solve the parameters of the improved Markov random field model, and the local energy of all image blocks is optimized.Experiments show that the proposed defect detection method gains high recall.
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2018, Vol. 31 
