Robust Defect Segmentation in Woven Fabrics
نویسندگان
چکیده
This paper describes a robust segmentation algorithm for the detection and localization of woven fabric defects. The essence of the presented segmentation algorithm is the localization of those events (i.e., defects) in the input images that disrupt the global homogeneity of the background texture. To this end, preprocessing modules, based on the wavelet transform and edge fusion, are employed with the objective of attenuating the background texture and accentuating the defects. Then, texture features are utilized to measure the global homogeneity of the output images. If these images are deemed to be globally nonhomogenous (i. e,, defects are present), a local roughness measure is used to localize the defects. The utility of this algorithm can be extended beyond the spec@ application in our work, that is, defect segmentation in woven fabrics. Indeed, in a general sense, this algorithm can be used to detect and to localize anomalies that reside in images characterized by ordered texture. The eflcacy of this algorithm has been tested thoroughly under realistic conditions and as a part of an on-line fabric inspection system. Using over 3700 images of fabrics, containing 26 different ppes of defects, the overall detection rate of our approach was 89% with a localization accuracy of less than 0.2 inches and a false alarm rate of 2.5%.
منابع مشابه
Unsupervised defect segmentation of patterned materials under NIR illumination
An unsupervised detection method for automatic flaw segmentation in patterned materials (textile, non-woven, paper) that has no need of any defect-free references or a training stage is presented in this paper. Printed materials having a pattern of colored squares, bands, etc. superimposed to the background texture can be advantageously analyzed using NIR illumination and a camera with enough s...
متن کاملDesigning Robust Hierarchically Textured Oleophobic Fabrics.
Commercially available woven fabrics (e.g., nylon- or PET-based fabrics) possess inherently re-entrant textures in the form of cylindrical yarns and fibers. We analyze the liquid repellency of woven and nanotextured oleophobic fabrics using a nested model with n levels of hierarchy that is constructed from modular units of cylindrical and spherical building blocks. At each level of hierarchy, t...
متن کاملRobust Potato Color Image Segmentation using Adaptive Fuzzy Inference System
Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...
متن کاملA novel approach in geometrical-mechanical analysis of plain woven fabrics; Initial load-extension behavior
متن کامل
Simulate the Dynamic Draping Behavior of Woven and Knitted Fabrics
In this article a practical mass-spring system was developed to simulate the draping of woven and knitted fabrics. The material properties important to fabric drape, including aerial density, tensile, shear, and bending properties were measured using the Kawabata Evaluation System and the experimental data were incorporated into the mass-spring model to simulate the dynamic draping behavior of ...
متن کامل