Fabric defect detection based on similarity location and superpixel segmentation

Aiming at the problem in defect detection and accurate contour segmentation of periodic texture fabric image, a method of fabric defect detection was proposed based on similarity location and superpixel segmentation techniques. The median filter and logarithm enhancement were applied for the detected image, and the saliency image of the enhancement image was estimated by frequency-tuned algorithm to facilitate the preprocessing of the detected image. Combining gray similarity detection parameters based on the normalized local mean difference with structural similarity detection parameters, a similarity metric function capable of measuring more types of periodic texture fabric images was constructed. The rough localization of defects was identified by thresholding the similarity measurement value of the enhancement image blocks. Finally, superpixel fine segmentation and binarization were performed on the rough localization image blocks, and the outliers were eliminated via connected domain analysis to obtain a complete defect contour. The experimental results show that, compared with the three conrentional methods, the proposed method has a higher accuracy in detecting the defects in the periodic texture fabric image, and the extracted defect contour is more accurate. Copyright No content may be reproduced or abridged without authorization.