Yarn-dyed fabric defect detection based on context visual saliency

In order to facilitate the effective detection of yarn-dyed fabric defects, a defect detecting method based on context visual saliency was proposed. Using this method, the fabric image was firstly divided into image patches of the same size according to the principle of context visual saliency. Following that, for every image patch, a number (K) of image patches, most similar to the concerned image patch were selected, and the sum of the differences among the K image patches and the image patch of concern were calculated. The calculated sum of the differences was then used to represent the saliency of center pixel of the image patches, thereby generating a visual saliency map. Finally, the threshold of the saliency map was segmented to obtain the detection result of the yarn-dyed fabric defect. In order to verify the validity of the algorithm, the yarn-dyed fabric regional defect image samples with looped weft, holes and netting of color dots, color stripes and color checks were detected. The experimental results show that the proposed algorithm can suppress the texture background and highlight the defect area of different types of fabrics and achieve the effective detection of fabric defects, which indicates the effectiveness of the method for detecting defects in yarn-dyed fabrics. Copyright No content may be reproduced or abridged without authorization.