Fabric flaw automatic detection method based on Support Vector data description theory

Abstract

The invention belongs to the field of automatic detection and control of textile quality and particularly relates to a method for automatically detecting defects of textiles based on support vector data description theory. The automatic textile defect detection based on computer vision is a more difficult one-class classification task in real world. In the invention, support vector data description (SVDD) of the advanced one-class classification method is applied in the textile defect detection field for the first time to obviate the problems present in textile defect detection of conventional two-class classification support vector machine, which is difficult to collect representative defect samples completely and at larger number and further fails to effectively train the detector. Additionally, the invention provides a robust new method for solving the optimization problem of parameters, in particular to scale parameter of gauss kernel function, related in SVDD training. The automatic textile defect detection system based on SVDD can prospectively and conveniently control false alarm rate (false detection rate) in practice and can obtain lower miss ratio at lower false alarm rate.

Description

technical field [0001] The invention belongs to the field of automatic detection and control of textile quality, in particular to an automatic detection method for fabric defects based on support vector data description theory. Background technique [0002] The automatic detection of fabric defects based on computer vision is a research hotspot and difficulty in the application of modern intelligent technology to monitor product quality in the past one or two decades. The automatic detection of fabric defects belongs to the category of pattern classification in essence, specifically, it uses various modern information technologies to realize the identification of normal areas and defect areas in fabric images. At present, the vast majority of textile factories still use manual cloth inspection, which has many disadvantages, such as low detection speed, inability to implement real-time detection, and the labor cost, labor intensity, and missed detection rate involved are also...

AI Technical Summary

Problems solved by technology

It is obviously unreasonable to use two types of classifiers for single-class classification problems: there is no guarantee that there are enough samples of fabric defects for training; Effective detection of

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