Abstract:

In order to improve the precision of fabric defects segmentation, an improved frequency-tuned salient (FT) algorithm is proposed for the preprocessing of fabric image. Firstly, the light source and camera are placed on both sides of the fabric to obtain the image, and the contrast ratio of defect area was strengthened by the difference of transmittance between normal area and defect area. Secondly, the non-local mean filter (NLM) was used instead of the Gauss filter in the FT method to enhance the cap ability of texture smoothing and denoising; and it is found that the NLM filter parameter has great influence on the accuracy of image segmentation. A method of parameter optimization using the average of inter-class maximum variance was proposed. Then, the improved FT algorithm was applied to the prepocessing of images to strengthen the contrast ratio of fabric defect area. Finally, OTSU algorithm was used to segment salient image of fabric defect. The experiments of image segmentation were carried out for two different fabric. The experimental result shows that the segmentation precision of fabric defects, including slab yarn, knot, broken warp, oil stain, hole and so on, can significantly increased with the improved FT algorithm.

Key words: fabric defect, non-local mean filter, frequency-tuned salient algorithm, image segmentation