Fabric defect detection based on a deep convolutional neural network using a two-stage strategy

Xiang, Jun; Wang, Jingan; Zhou, Jian; Meng, Shuo; Pan, Ruru; Gao, Weidong

doi:10.1177/0040517520935984

Cited by 59 publications

(30 citation statements)

References 33 publications

(46 reference statements)

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“…Some researchers utilize the modified Faster R-CNN model for fabric defect detection [85][86][87]. Jun et al [88] propose a framework that utilized the Inception-V1 model and LeNet-5 model. is approach includes local defect prediction in the first stage and global defect recognition in the second stage.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

et al. 2021

Security and Communication Networks

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Defects in the textile manufacturing process lead to a great waste of resources and further affect the quality of textile products. Automated quality guarantee of textile fabric materials is one of the most important and demanding computer vision tasks in textile smart manufacturing. This survey presents a thorough overview of algorithms for fabric defect detection. First, this review briefly introduces the importance and inevitability of fabric defect detection towards the era of manufacturing of artificial intelligence. Second, defect detection methods are categorized into traditional algorithms and learning-based algorithms, and traditional algorithms are further categorized into statistical, structural, spectral, and model-based algorithms. The learning-based algorithms are further divided into conventional machine learning algorithms and deep learning algorithms which are very popular recently. A systematic literature review on these methods is present. Thirdly, the deployments of fabric defect detection algorithms are discussed in this study. This paper provides a reference for researchers and engineers on fabric defect detection in textile manufacturing.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

et al. 2021

Security and Communication Networks

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“…However, the technique failed to increase the detection accuracy. Xiang Jun et al [23] devised a learning-based model for the automated discovery of fabric defects. At first, the fixed-size square slider was utilized for cropping the original image to a certain step.…”

Section: Literature Surveymentioning

confidence: 99%

Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

Biradar¹,

Sheeparamatti²,

Patil³

2021

Sens Imaging

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The rising rates of costs in labor and growth of computerization in fabric industries made defect detection in fabric a promising domain. For a huge time, manual discovery is extensively utilized in textile industries by trained staff that results in high cost. Meanwhile, the strict quality assessment is done by modern textile industries, which made automatic fabric defect detection a reliable choice. Since defect detection is an important and challenging aspect of modern industrial manufacturing, it is necessary to determine the quality and acceptability of garments and to reduce the cost and time waste caused by defects. Different methods are in practice for effective detection of fabric defects, however, they limit due to many reasons. Thus we proposed a new method named Competitive Cat Swarm Optimizer (CCSO) based Deep neuro-fuzzy network (DNFN) for effective fabric detection. Here, the pre-processing is performed with a median filter for eliminating noise contained in the image. Furthermore, features are extracted that involves Tetrolet transform-based features, statistical features, like energy, entropy, homogeneity, contrast, correlation, and texture feature, like Local gradient pattern. The data augmentation is carried out based on obtained features to make it apposite for processing. The defect detection is carried out using RideNN and DNFN. Here, the training of DNFN is done using the proposed CCSO, which is devised by combining Cat Swarm Optimization and Competitive Swarm Optimizer. The correlation is performed on the outputs of RideNN and DNFN for generating the final output of fabric defect detection. The performance of the proposed CCSO-based DNFN is compared with the different existing methods and the proposed CCSO-based DNFN outperforms with the highest specificity of 0.920, the accuracy of 0.919, and sensitivity of 0.916.

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“…The proposed method can effectively extract discriminative features and shared features simultaneously of multi-class fabric textures. Jun et al [22] proposed a fabric defect detection algorithm based on deep convolutional neural network. The algorithm blocks the original image, uses inception-V1 model to predict local area defects, and uses Lenet-5 model to identify fabric defect types.…”

Section: Related Workmentioning

confidence: 99%

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

Kang

Zhang

2020

IEEE Access

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Due to the complex diversity of both fabric texture and defect, fabric defect detection is a challenge topic. However, most of existing defect detection methods can still detect only one type of fabric defects. In order to solve this problem, we propose a universal and adaptive defect detection algorithm based on dictionary learning for detecting various defects of different fabric texture. Firstly, in order to make the image more balanced and improve the detection accuracy, according to the complexity of fabric texture and the degree of brightness and darkness of the background, we segment defect-free image according to a certain size to obtain the image joint matrix. In order to make the algorithm more universal, next we form a random dictionary instead of over-complete dictionary and fixed dictionary by randomly select feature columns from the image joint matrix, which can extract the most important texture and background information of fabric. Then, we use the Orthogonal Matching Pursuit algorithm to obtain the sparse representation of defect-free image. On this basis, we use the K-Singular Value Decomposition algorithm to update the dictionary iteratively to get the final dictionary. Afterward, the defective image is reconstructed with the learned dictionary to obtain the reconstruction error. Finally, the correction coefficient is obtained through reconstruction error analysis, then the corresponding adaptive threshold is used to detect and mark the location of the defects. The performance of the algorithm is evaluated on the fabric database of raw fabric, yarn-dyed fabric and patterned fabric. The experimental results show that the proposed algorithm has good universality, adaptability and a high detection success rate for different types of fabrics. Particularly, the average detection success rate of dark-red fabric and dot-patterned fabric can reach 100%, and the Precision and the Recall of defect detection evaluation index can also reach 100%.

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Fabric defect detection based on a deep convolutional neural network using a two-stage strategy

Cited by 59 publications

References 33 publications

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

Fabric Defect Detection in Textile Manufacturing: A Survey of the State of the Art

Fabric Defect Detection Using Competitive Cat Swarm Optimizer Based RideNN and Deep Neuro Fuzzy Network

A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning

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