Yarn-Dyed fabric defect detection is an important part of the textile production process, in which rapid and accurate detection is the main challenge in textile industry. However, the performance of defect detection largely depends on whether the manually designed features can properly represent the features of the defects. In this paper, a new detection algorithm for automatic fabric defect detection using the deep convolutional neural network (CNN) is put forward. Our defect detection algorithm is based on three main steps. In the first step, a preprocessing stage decomposes the fabric image into local patches and labels each local patch accordingly. In the second step, labeled fabric samples are transmitted to deep CNN for pre-training. Finally, defects are detected during image inspection that trained classifier slides over the entire fabric image and returns the category and position of each local patches to achieve defect detection. The proposed method was validated on two public and one self-made fabric databases. By comparing manually designed image processing solutions with other deep CNN networks for feature extraction methods, the experiments show that the proposed method can inspect defects at a higher accuracy compared with some existing methods.
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.
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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