TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Jing, Junfeng
A1  - Wang, Zhen
A1  - Rätsch, Matthias
A1  - Zhang, Huanhuan
T1  - Mobile-Unet: An efficient convolutional neural network for fabric defect detection
JF  - Textile Research Journal
N2  - 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.
KW  - fabric defect
KW  - deep learning
KW  - Mobile-Unet
KW  - efficient convolutional neural network
Y1  - 2020
SN  - 0040-5175
SS  - 0040-5175
U6  - https://doi.org/10.1177/0040517520928604
DO  - https://doi.org/10.1177/0040517520928604
SP  - 1
EP  - 13
S1  - 13
PB  - Sage
CY  - London
ER  -