Fabric Defect Detection Algorithm Based on Convolution Neural Network and Low-Rank Representation

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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Fabric defect detection based on deep-handcrafted feature and weighted low-rank matrix representation

Journal of Engineered Fibers and Fabrics ◽

10.1177/15589250211008453 ◽

2021 ◽

Vol 16 ◽

pp. 155892502110084

Author(s):

Chunlei Li ◽

Ban Jiang ◽

Zhoufeng Liu ◽

Yan Dong ◽

Shuili Tang ◽

...

Keyword(s):

Defect Detection ◽

Matrix Representation ◽

Singular Values ◽

Low Rank ◽

Fabric Defect Detection ◽

Rank Matrix ◽

Fabric Defect ◽

The Matrix ◽

Low Rank Representation ◽

Low Rank Matrix

In the process of textile production, automatic defect detection plays a key role in controlling product quality. Due to the complex texture features of fabric image, the traditional detection methods have poor adaptability, and low detection accuracy. The low rank representation model can divide the image into the low rank background and sparse object, and has proven suitable for fabric defect detection. However, how to further effectively characterize the fabric texture is still problematic in this kind of method. Moreover, most of them adopt nuclear norm optimization algorithm to solve the low rank model, which treat every singular value in the matrix equally. However, in the task of fabric defect detection, different singular values of feature matrix represent different information. In this paper, we proposed a novel fabric defect detection method based on the deep-handcrafted feature and weighted low-rank matrix representation. The feature characterization ability is effectively improved by fusing the global deep feature extracted by VGG network and the handcrafted low-level feature. Moreover, a weighted low-rank representation model is constructed to treat the matrix singular values differently by different weights, thus the most distinguishing feature of fabric texture can be preserved, which can efficiently outstand the defect and suppress the background. Qualitative and quantitative experiments on two public datasets show that our proposed method outperforms the state-of-the-art methods.

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Fabric defect detection based on feature fusion of a convolutional neural network and optimized extreme learning machine

Textile Research Journal ◽

10.1177/00405175211044794 ◽

2021 ◽

pp. 004051752110447

Author(s):

Zhiyu Zhou ◽

Wenxiong Deng ◽

Zefei Zhu ◽

Yaming Wang ◽

Jiayou Du ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Extreme Learning Machine ◽

Defect Detection ◽

Feature Fusion ◽

Principal Component ◽

Detection Algorithm ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Learning Machine

Aiming to accurately detect various defects in the fabric production process, we propose a fabric defect detection algorithm based on the feature fusion of a convolutional neural network (CNN) and optimized extreme learning machine (ELM). Firstly, we use transfer learning to transfer the parameters of the first 13 convolutional layers and first two fully connected layers of a VGG16 network model as pre-trained by ImageNet to the initial model and fine-tune the parameters. Subsequently, the fine-tuned model is used as a feature extractor to extract features of RGB images and their corresponding L-component images. A principal component analysis is used to reduce the dimensionality of the features and fuse the reduced features. The moth flame optimization (MFO) algorithm is used to initialize the optimization variables of a parallel chaotic search (PCS) algorithm, and the PCS algorithm (as optimized by the MFO algorithm) is used to optimize the input weight and bias of the ELM (i.e., the PCS-MFO-ELM (PMELM)). Finally, the PMELM is used to replace the softmax classifier of the CNN to classify and detect fabric defect features. The experimental results show that on the amplified TILDA dataset, the precision, recall, F1-score, and accuracy rates of this algorithm for fabric holes, stains, warp breaks, dragging, and folds in fabric can reach 98.57%, 98.52%, 98.52%, and 98.50%, respectively, that is, higher than those of other algorithms. Through a validity experiment, this method is shown to be suitable for defect detection for unpatterned fabrics, regular patterned fabrics, and irregularly patterned fabrics.

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Fabric defect detection based on deep-feature and low-rank decomposition

Journal of Engineered Fibers and Fabrics ◽

10.1177/1558925020903026 ◽

2020 ◽

Vol 15 ◽

pp. 155892502090302 ◽

Cited By ~ 1

Author(s):

Zhoufeng Liu ◽

Baorui Wang ◽

Chunlei Li ◽

Miao Yu ◽

Shumin Ding

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Defect Detection ◽

Low Rank ◽

Multi Scale ◽

Fabric Defect Detection ◽

Decomposition Model ◽

Fabric Defect ◽

Rank Decomposition ◽

Low Rank Decomposition

Fabric defect detection plays an important role in controlling the quality of textile production. In this article, a novel fabric defect detection algorithm is proposed based on a multi-scale convolutional neural network and low-rank decomposition model. First, multi-scale convolutional neural network, which can extract the multi-scale deep feature of the image using multiple nonlinear transformations, is adopted to improve the characterization ability of fabric images with complex textures. The effective feature extraction makes the background lie in a low-rank subspace, and a sparse defect deviates from the low-rank subspace. Then, the low-rank decomposition model is constructed to decompose the feature matrix into the low-rank part (background) and the sparse part (salient defect). Finally, the saliency maps generated by the sparse matrix are segmented based on an improved optimal threshold to locate the fabric defect regions. Experimental results indicate that the feature extracted by the multi-scale convolutional neural network is more suitable for characterizing the fabric texture than the traditional hand-crafted feature extraction methods, such as histogram of oriented gradient, local binary pattern, and Gabor. The adopted low-rank decomposition model can effectively separate the defects from the background. Moreover, the proposed method is superior to state-of-the-art methods in terms of its adaptability and detection efficiency.

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