(1. Laboratory of Optics, Terahertz and Non destructive Testing & Evaluation, 

 School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;

 2. Fujian Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou 350108, China)

 Abstract: In order to realize on line defect detection of fabric in real industry, an abnormal fabric detection method based on One Class Support Vector Machine (OCSVM) was proposed. The fabric images were collected by a CCD camera and were filtered by median filter before the features of sub images were extracted from the divided rectangular areas. Two groups of effective feature vectors were decided by experiments. After the normalization and dimensionality reduction by using principal component analysis, the features were employed in the training of OCSVM, which subsequently could be used to locate and label the abnormal regions. The defective regions could be obtained through the combination of detection results obtained from the two different groups of feature factors. The experimental results indicate that the algorithm could correctly identify different defects and could effectively reduce the false alarm rate and missed detection rate. It provided an available solution to solve the problem of the difficulty in acquiring enough defective samples in the practical production and has a good generalization performance to the unknown test samples. The algorithm can meet the demand of industrial application.

 Key words: fabric; defect detection; machine learning; support vector machine(SVM)