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

作者：Liu Zhoufeng^*; Li Chunlei; Zhao Quanjun; Liao Liang; Dong Yan

来源：International Journal of Clothing Science and Technology, 2015, 27(5): 738-750.

DOI：10.1108/IJCST-02-2014-0028

• Defect detection
• Fabric defect
• Salient detection

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.

• 出版日期2015
• 单位中原工学院