Multistage GAN for Fabric Defect Detection

Liu, J.; Wang, Chaoyue; Su, Hua; Du, Bo; Tao, Dacheng

doi:10.1109/tip.2019.2959741

Cited by 171 publications

(92 citation statements)

References 30 publications

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“…The evaluation criteria utilized in our experiments include four parameters: DSR, Precision, Recall and Fmeasure [31]- [32]. The DSR means detection success rate.…”

Section: A the Selection Of Dictionary Atomic Numbermentioning

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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“…The evaluation criteria utilized in our experiments include four parameters: DSR, Precision, Recall and Fmeasure [31]- [32]. The DSR means detection success rate.…”

Section: A the Selection Of Dictionary Atomic Numbermentioning

confidence: 99%

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

Kang

Zhang

2020

IEEE Access

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“…The unique and ubiquitous characteristics of CEC arise the main challenges for intrusion detection [23]. Motivated by that, we propose a generative adversarial network (GAN)based mechanism to implement intrusion detection [24]. GAN consists of two networks, i.e., a discriminator and a generator.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Nie

Wang

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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The Social Internet of Things (SIoT) now penetrates our daily lives. As a strategy to alleviate the escalation of resource congestion, collaborative edge computing (CEC) has become a new paradigm for solving the needs of the Internet of Things (IoT). CEC can provide computing, storage, and network connection resources for remote devices. Because the edge network is closer to the connected devices, it involves a large amount of users' privacy. This also makes edge networks face more and more security issues, such as Denial-of-Service (DoS) attacks, unauthorized access, packet sniffing, and man-inthe-middle attacks. To combat these issues and enhance the security of edge networks, we propose a deep learning-based intrusion detection algorithm. Based on the generative adversarial network (GAN), we designed a powerful intrusion detection method. Our intrusion detection method includes three phases. First, we use the feature selection module to process the collaborative edge network traffic. Second, a deep learning architecture based on GAN is designed for intrusion detection aiming at a single attack. Finally, we propose a new intrusion detection model by combining several intrusion detection models that aim at a single attack. Intrusion detection aiming at multiple attacks is realized through the designed GAN-based deep learning architecture. Besides, we provide a comprehensive evaluation to verify the effectiveness of the proposed method. Index Terms-Collaborative edge computing (CEC), generative adversarial network (GAN), intrusion detection, social internet of things (SIoT).

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“…However, the detected objects occupy larger regions in images, and the shapes of the detected objects are moderate aspect ratios, such as people, airplanes, etc. Many methods, such as GAN [19], deep convolutional neural network [20] and YOLO v3 [21], are effective in detecting fabric defects, but cannot detect exceedingly small and extreme aspect ratios fabric defects well.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fabric Defect Detection Method Using PRAN-Net

et al. 2020

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Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

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Multistage GAN for Fabric Defect Detection

Cited by 171 publications

References 30 publications

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

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

Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach

Automatic Fabric Defect Detection Method Using PRAN-Net

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