A real-time computer vision-based platform for fabric inspection part 1: algorithm

Abstract

Developing an efficient real-time detection algorithm is quite important for an automated inspection system. This paper presents a practical method based on local singular value decomposition (SVD) and normalised cross-correlation (NCC) for real-time defect detection in woven fabrics. As fabric-textured images exhibit high periodicity among the repeated sub-patterns, non-defective or normal image samples (image patches) can be efficiently approximated as a linear combination of the basis vectors (BVs) obtained via SVD. Since these BVs are recovered from normal samples, they will only capture the key structural features of the non-defective images. When using the BVs to model new samples, we can expect defective or abnormal samples with structural features not found in normal cases will incur substantial approximation errors. Therefore, complex defect detection can be converted to a template matching problem, where the robust NCC is utilised to measure disparities between the original and its approximation for defect identification. Experimental results on various real-world fabrics exhibit accurate defect detection with low false alarm rate, and we also conduct a comparison with a feature extraction-based method to further confirm the effectiveness of our algorithm.

Rail Corrugation Detection and Characterization Using Computer Vision

“…Various industrial applications for defect inspection have been devised [20][21][22][23]. Remarkably, Li et al [20] proposed a corrugation identification system based on local frequency features of a segmented track image.…”

On the Use of Binary Features in a Rule-Based Approach for Defect Detection on Patterned Textiles

“…uniform, random, and patterned texture. Although the detection of defects on uniformly and randomly textured fabrics has been extensively studied [6]- [11], the analysis of fabrics with patterned texture is still a challenging task [12], [13].…”