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Real-time implementation of fabric defect detection based on variational automatic encoder with structure similarity

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Abstract

Automatic detection of fabric defects based on machine vision is an important topic in the quality control of cotton textile factories. There are many kinds of defects in fabric production, it is very difficult to classify the defects automatically. In recent years, deep learning image processing technology based on a convolutional neural network (CNN) can train and extract features of the target image automatically. Since a large number of defect samples cannot be collected completely, we compared unsupervised learning algorithms based on CNN, including auto encoder (AE), variational automatic encoder (VAE), and generative adversarial networks (GAN). Because of the large amount of calculation and the difficulty of training in GAN, we chose AE and VAE codec networks and then introduced mean structural similarity (MSSIM) as network training loss function to improve the performance that only used \({L}_{p}\)-distance loss function for image brightness comparison. After training finished, the authors used the trained model to obtain target defects from SSIM residual maps between input and reconstruct images. According to the evaluation results, we finally implemented a fabric defect detection system based on VAE on Jetson TX2 from Nvidia Corporation, USA. The optimized algorithm can meet the real-time requirements of the project and realize its popularization and application.

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Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Wei Wei, Dexiang Deng & Lin Zeng

  2. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, 430072, China

    Chen Zhang

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  1. Wei Wei
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  2. Dexiang Deng
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  3. Lin Zeng
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  4. Chen Zhang
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Correspondence to Wei Wei.

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Wei, W., Deng, D., Zeng, L. et al. Real-time implementation of fabric defect detection based on variational automatic encoder with structure similarity. J Real-Time Image Proc 18, 807–823 (2021). https://doi.org/10.1007/s11554-020-01023-5

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  • DOI: https://doi.org/10.1007/s11554-020-01023-5

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