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13 September 2019 Automatic detection of microfabric defects with Gabor fractal network
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Abstract

Automatic detection of fabric defects is an important process in the textile industry, which is required to locate and classify microdefects from a large fabric image. We propose a learning-based system for automatic detection of microfabric defects. A segmentation algorithm based on fractal and gray features is applied to extract microdefect regions. Gabor fractal network is designed to further improve identification ability of this approach. The proposed network achieves superior performance in terms of detection accuracy with a much smaller model size. The best testing accuracy rates on dark line, hole, broken yarn, and dirt are 96.9%, 98.0%, 92.9%, and 98.8%, respectively. Experimental results demonstrate the effectiveness of the proposed scheme in defect detection for microfabrics. The proposed system has great potential for automatic detection of microfabric defects.

© 2019 SPIE and IS&T 1017-9909/2019/$28.00 © 2019 SPIE and IS&T
Mohan Chen, Tengchao Huang, Yongjian Feng, Zhigang Wang, and Huangping Yan "Automatic detection of microfabric defects with Gabor fractal network," Journal of Electronic Imaging 28(5), 053005 (13 September 2019). https://doi.org/10.1117/1.JEI.28.5.053005
Received: 5 May 2019; Accepted: 13 August 2019; Published: 13 September 2019
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