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Open AccessArticle

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

by Shuang Mei, Yudan Wang and Guojun Wen *,†
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2018, 18(4), 1064; https://doi.org/10.3390/s18041064
Received: 8 February 2018 / Revised: 28 March 2018 / Accepted: 29 March 2018 / Published: 2 April 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates. View Full-Text
Keywords: fabric defect detection; unsupervised learning; deep neural network; convolutional denoising autoencoder; Gaussian pyramid fabric defect detection; unsupervised learning; deep neural network; convolutional denoising autoencoder; Gaussian pyramid
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Mei, S.; Wang, Y.; Wen, G. Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model. Sensors 2018, 18, 1064.

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