Fabric Defect Detection Based on Distortion Correction and Visual Salient Features
LONG Hanbin1, DI Lan1, LIANG Jiuzhen2
1. School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122;
2. College of Information Science and Engineering,Changzhou University,Changzhou 213164
Aiming at fabric defect detection with complex patterns,a fabric defect detection method based on distortion correction and visual salient features is proposed.Firstly,the image period is calculated to obtain the best block template,and then the image distortion is corrected according to the template.Secondly,the image is decomposed into texture layer and cartoon layer,and only the cartoon layer with the main features of the image is retained.Then,the improved context-aware saliency algorithm is applied to obtain the saliency feature of the image cartoon layer,so that the defects with high saliency features are separated from the background with low saliency features.Finally,the K-means clustering algorithm is utilized to highlight defects and complete defect detection.Experiments show that the proposed method achieves a high average recall rate for star,box and dot pattern fabrics,and the average recall precision effect of the proposed method is superior to that of the existing methods.
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