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.
Fabric Defect Detection Algorithm Based on Local Neighborhood Analysis