Fabric defect detection using an integrated model of bottom-up and top-down visual attention

In this paper, we introduce visual attention mechanism from the human vision system to detect fabric defects, and propose the integrated computational model of top-down and bottom-up visual attention. Firstly, utilizing data driven of bottom-up visual attention generates the overall saliency map to pop out fabric defects. Secondly, using target feature driven (task driven) of top-down visual attention forms region of interest (ROI) of fabric defects. Finally, the fabric defects are segmented from ROI using the threshold. Experimental results show that compared with the traditional detection methods, the proposed algorithm can segment accurately common defects from fabric images and enhance detection rate, and it has strong universality for different fabric texture, which can provide the possibility for the realization of automatic fabric defect detection.