Automatic fabric defect detection using a deep convolutional neural network

2019 ◽  
Vol 135 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Jun‐Feng Jing ◽  
Hao Ma ◽  
Huan‐Huan Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Deep Convolutional Neural Network ◽  
Fabric Defect Detection ◽  
Fabric Defect

Fabric Defect Detection Using Convolutional Neural Network

2020 ◽  
Vol 12 (05-SPECIAL ISSUE) ◽  
pp. 950-955
Author(s):  
Eldho Paul ◽  
Nivedha K ◽  
Nivethika M ◽  
Pavithra V ◽  
Priyadharshini G
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 70130-70140 ◽  
Author(s):  
Wenbin Ouyang ◽  
Bugao Xu ◽  
Jue Hou ◽  
Xiaohui Yuan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect

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

2020 ◽  
pp. 004051752092860 ◽  
Author(s):  
Junfeng Jing ◽  
Zhen Wang ◽  
Matthias Rätsch ◽  
Huanhuan Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Production Efficiency ◽  
Detection Methods ◽  
Median Frequency ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Model Training

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.

Bag of tricks for fabric defect detection based on Cascade R-CNN

2020 ◽  
pp. 004051752095522
Author(s):  
Feng Li ◽  
Feng Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Input Image ◽  
Defect Size ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Single Input

In this paper, a bag of tricks is proposed to improve the precision of fabric defect detection. Although the general state-of-the-art convolutional neural network detection algorithm can achieve a better detection effect, in fact, the detection precision still has enough room to improve on fabric defect detection. Therefore, we propose three tricks to further improve the precision. Firstly, we use multiscale training, which scales the single input image into a number of images of different resolutions for training, so as to be able to adapt to the box distribution of different scales. Secondly, we use the dimension clusters method. By observing the distribution of the width and the height of the defect size in the fabric dataset, we find that the distribution of the defect size in the dataset is extremely unbalanced and the size span is large. We believe that the training results of the default prior boxes setting might not be optimal, so we conduct dimensional clustering for the width and height of the defect size of the dataset, so as to make the network model easier to learn. Thirdly, we use soft non-maximum suppression instead of traditional non-maximum suppression to avoid the situation that the same kinds of defect category in the dataset are overlapped and eliminated as repeated detection. With this bag of tricks, we effectively improve the precision of fabric defect detection by 8.9% mAP on the basis of the baseline of state-of-the-art convolutional neural network detection algorithm.

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