Detection and evaluation of fabric defects using warp-weft statistical analysis

Kaixin Liu; R. Saminathan; Hung-Kun Shih; Stefano Sfarra; Jianguo Yang; Yi Liu; Yuan Yao

doi:10.1117/12.2612708

18 April 2022 Detection and evaluation of fabric defects using warp-weft statistical analysis

Kaixin Liu, R. Saminathan, Hung-Kun Shih, Stefano Sfarra, Jianguo Yang, Yi Liu, Yuan Yao

Proceedings Volume 12049, NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World; 120490F (2022) https://doi.org/10.1117/12.2612708
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States

Abstract

Defect detection is of great significance for assessing and controlling the quality of fabrics. However, most traditional detection processes rely on manual visual inspection, resulting in low detection efficiency, ambiguous detection results, and high monitoring costs. In this work, a centroid warp-weft graph-based (C2WG) statistical analysis method is proposed for the detection and evaluation of fabric defects. To reflect the fabric texture variation, the C2WG method is first proposed to find abnormal texture centers. Subsequently, by dual monitoring of local slope and curvature, the location of the abnormal centroid can be accurately determined as texture defects and displayed. Finally, the defect evaluation results under different detection accuracy are obtained by changing the monitoring threshold. Consequently, the defects are classified into different classes. A case study on an industrial design fabric product validates the good performance of the proposed method.

Citation Download Citation

Kaixin Liu, R. Saminathan, Hung-Kun Shih, Stefano Sfarra, Jianguo Yang, Yi Liu, and Yuan Yao "Detection and evaluation of fabric defects using warp-weft statistical analysis", Proc. SPIE 12049, NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World, 120490F (18 April 2022); https://doi.org/10.1117/12.2612708

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