Ms. AMei Dong Profile

AMei Dong

Student at Xi'an Polytechnic Univ

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SPIE Journal Paper | 26 September 2017

Yarn-dyed fabric defect classification based on convolutional neural network

Junfeng Jing, Amei Dong, Pengfei Li, Kaibing Zhang

OE, Vol. 56, Issue 09, 093104, (September 2017) https://doi.org/10.1117/12.10.1117/1.OE.56.9.093104

KEYWORDS: Convolution, Convolutional neural networks, Image fusion, Feature extraction, Data modeling, Statistical modeling, Image classification, Performance modeling, Optical engineering, Neurons

Read Abstract +

Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

Proceedings Article | 21 July 2017 Paper

Yarn-dyed fabric defect classification based on convolutional neural network

Junfeng Jing, Amei Dong, Pengfei Li

Proceedings Volume 10420, 104202T (2017) https://doi.org/10.1117/12.2281978

KEYWORDS: Neural networks

Read Abstract +

Considering that the manual inspection of the yarn-dyed fabric can be time consuming and less efficient, a convolutional neural network (CNN) solution based on the modified AlexNet structure for the classification of the yarn-dyed fabric defect is proposed. CNN has powerful ability of feature extraction and feature fusion which can simulate the learning mechanism of the human brain. In order to enhance computational efficiency and detection accuracy, the local response normalization (LRN) layers in AlexNet are replaced by the batch normalization (BN) layers. In the process of the network training, through several convolution operations, the characteristics of the image are extracted step by step, and the essential features of the image can be obtained from the edge features. And the max pooling layers, the dropout layers, the fully connected layers are also employed in the classification model to reduce the computation cost and acquire more precise features of fabric defect. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show the capability of defect classification via the modified Alexnet model and indicate its robustness.

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