TY - JOUR
AU - Ding, Song
AB - This paper introduces a hybrid technique designed to identify fabric defects in the early stages of textile manufacturing production. Manual detection of fabric abnormalities is a challenging task, prompting the development of an intelligent system that leverages techniques such as multi-scale Gabor transform and gray level co-occurrence matrix (GGCM) for fabric defect diagnosis. In the preprocessing phase, the input image undergoes down-sampling and quantization. Subsequently, efficient noise removal is achieved using a median filter, followed by the extraction of the region of interest through histogram matching. During the segmentation stage, a band-pass filter is formed by manipulating a 1-D Gaussian filter in frequency space, creating what is known as a Circular Gaussian Filter (CGF) by rotating it off-center. A CGF is distinguished by its unique definition involving the specification of both a central frequency and a frequency band. Typically, fabric flaws appear as brighter regions in the processed image. Defect regions are identified by dynamically establishing a threshold through histogram analysis of the CGF-filtered image. The subsequent step involves extracting defects by leveraging gray level co-occurrence matrix (GLCM) features. This method is rigorously compared with current mainstream algorithms across various types of fabric defects, accompanied by an analysis of its strengths and weaknesses. The experimental results demonstrate that the algorithm achieves a high detection success rate and precision in defect marking, indicating promising potential for practical applications.
TI - A real-time fabric defect inspection based on improved multi-scale Gabor filter
JF - Journal of Computational Methods in Sciences and Engineering
DO - 10.1177/14727978241300076
DA - 2025-01-01
UR - https://www.deepdyve.com/lp/ios-press/a-real-time-fabric-defect-inspection-based-on-improved-multi-scale-x6exa090zE
SP - 211
EP - 222
VL - 25
IS - 1
DP - DeepDyve
ER -