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Mobile-Unet: An efficient convolutional neural network for fabric defect detection

  • Deep learning–based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning–based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance.

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Metadaten
Author of HS ReutlingenRätsch, Matthias
DOI:https://doi.org/10.1177/0040517520928604
ISSN:0040-5175
eISSN:1746-7748
Erschienen in:Textile research journal : TRJ
Publisher:Sage
Place of publication:London
Document Type:Article
Language:English
Year of Publication:2020
Tag:Mobile-Unet; deep learning; efficient convolutional neural network; fabric defect
Page Number:13
First Page:1
Last Page:13
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:660 Technische Chemie
Open Access?:Nein
Licence (English):License Logo  Lizenzbedingungen Sage