Fabric Defect Detection Algorithm Based on Image Saliency Region and Similarity Location

In order to solve the problem of defect detection and to contour accurate segmentation of periodic texture fabric images, a fabric defect detection method based on saliency region and similarity location is proposed. Firstly, the image to be detected was processed by color space conversion, Gaussian filtering, and contrast enhancement, and a frequency-tuned (FT) salient region detection algorithm was used to estimate a saliency map of the enhanced image. The fabric image was divided into image blocks of the same size with overlapping areas through a sliding window, and then the statistical parameters of each image block were calculated. The outliers in the statistical parameters were filtered out using inter-quartile range (IQR). Through the positioning and processing of image defects, abnormal elimination was carried out, and the defect outline was finally obtained. The experimental results show that the method proposed in this paper has better performance in terms of qualitative characterization of Acc, Precision, Recall, and F1 score.