Fabric defect detection method based on single-classification support vector machine (SVM)

Abstract

The invention discloses a fabric defect detection method based on a single-classification support vector machine (SVM). The method comprises the following steps: obtaining a defect-free fabric image, optimizing parameters of a Gabor filter by use of an RDPSO algorithm, and constructing a single optimal Gabor filter most adapted to a defect-free fabric image texture feature; optimizing parameters of the single-classification SVM by use of the RDPSO algorithm; performing Gabor convolution filtering on a fabric image to be detected; extracting one group of texture features on the image after filtering on the basis of GLCM; and performing defect determining by use of the single-classification SVM. According to the invention, by use of the single optimal Gabor filter, the detection speed can be effectively improved and the requirement for real-time performance of a system is ensured; and by taking the single-classification SVM as a defect determining method, the problems of local extreme values, over-learning, under-learning and the like by use of a conventional statistical mode identification method can be avoided, the generalization capability of a system can be effectively improved, and the requirement for detection accuracy of the system can be guaranteed.

Description

Technical field: [0001] The invention belongs to the technical field of pattern recognition, in particular to a method for detecting fabric defects based on a single classification support vector machine. Background technique: [0002] At present, the textile industry is facing a severe test. Improving product quality and reducing production costs have become the key factors for the survival of enterprises. Fabric defects seriously affect product quality, resulting in lower fabric prices and reduced corporate income. Relying on machine vision to automatically detect defects can be an effective way to improve product quality and reduce production costs. Compared with manual visual inspection, the automatic inspection method based on machine vision has the advantages of high stability and is not affected by factors such as the emotion, physical condition and environmental interference of the inspector. In addition, after using the defect automatic detection system, when the ...

AI Technical Summary

Problems solved by technology

But at present, most textile enterprises still use manual methods for defect detection. One of the reasons is that the defect detection ability of the proposed system is not ideal enough and needs to be further improved.

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