Blind detection of fabric defects using multiple image features

Abstract

This paper proposes a blind detection of fabric defects using multiple image features. The aim of proposed method is to detect the new types of fabric defects that have not been learned. For general learning of image features, this paper first learns the characteristics of image features between normal and defective image patches for various types of fabric structures and defects. The image features are frequency coefficients, color histogram, and edge orientation histogram. The mean vectors of 3 image features are calculated, and the vector distance distributions of normal and defective patches are learned using support vector machine. Since the decision boundary is determined from general distributions of image features, the proposed method is not restricted to the detect types and fabric structures which have not used in the learning phase. According to experiments with the real fabric images and defects types that have not been learned, the proposed method detects fabric defects with 96.4% accuracy at 0.4% failure including false positive and false negative errors. This result outperforms the usual CNN (Convolutional Neural Network) approaches.