A fabric defect detection algorithm via context-based local texture saliency analysis

Abstract

Purpose

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis.

Design/methodology/approach

In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach.

Findings

The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively.

Originality/value

In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

Citation

Liu, Z., Li, C., Zhao, Q., Liao, L. and Dong, Y. (2015), "A fabric defect detection algorithm via context-based local texture saliency analysis", International Journal of Clothing Science and Technology, Vol. 27 No. 5, pp. 738-750. https://doi.org/10.1108/IJCST-02-2014-0028