Draw Textured Yarn Packages Hairiness Defect Detection Based on the Multi-directional Anisotropic Gaussian Directional Derivative

Draw textured yarn (DTY) packages is a significant raw material in manufacturing. Various defects will be generated on surface during production and transportation, of which hairiness is the most common and intractable defect. Many methods have been applied for fabric surface defect detection, but little research is aimed at DTY packages hairiness defects. In order to achieve the accuracy of DTY packages hairiness detection in industrial production, a method based on multi-directional anisotropic Gaussian directional derivatives was proposed to accomplish the DTY packages hairiness defect detection. Firstly, the original defect images were obtained by a device consisting of plane array light source, camera, and computer with image processing algorithm. Secondly, the gradient information of DTY packages images was constructed by anisotropic Gaussian directional derivative to characterize the defect. Then image response maps with all directions were fused to obtain the final response map. After that, a special difference of median (DOM) filter was proposed to remove useless information. Finally, the segmentation result was obtained by threshold method and morphological processing. Compared with various classical methods, the proposed method obtained the best performance in our evaluation experiments about DTY packages hairiness detection.