Detection of Bobbin Yarn Surface Defects by Visual Saliency Analysis

Abstract

In order to solve the problem of unstable quality of the traditional bobbin yarn surface defect detection, a method for detecting the surface defect of bobbin yarn using visual saliency analysis is proposed. Firstly, the Difference of Gaussian algorithm is used to suppress the texture of the image, and the contrast between the defect area and the background is enhanced. Then, the method of wavelet threshold is applied to filter the noise in the images after the Difference of Gaussian, and the detailed features of the defect area are preserved while the noise interference is eliminated. Finally, the frequency tuned visual saliency algorithm is used to extract and segment the defective regions accurately. After the process about the Difference of Gaussian and wavelet threshold denoising, the original image of the bobbin yarn can make the image highlight the feature information of the defect area on the basis of ensuring elimination of the background texture and noise, and facilitate the detection of the defect area by combining visual saliency analysis. In the detection of the accuracy and integrity of the defect area, the detection method of this paper is better than the control method, and the defect segmentation result is more accurate. The proposed method for detecting the surface defect of the bobbin can not only completely eliminate the influence of the background texture of the bobbin surface, but also accurately and completely detect the surface defect of the bobbin, and the defect details remain intact. In this paper, the effectiveness of the algorithm is further verified by objective tests. The precision and recall rate of the test are 95.15 % and 98.00 %, respectively, which can meet the needs of actual detection.