Steel Surface Defect Detection Using GAN and One-Class Classifier

K, Liu; Li, Aimei; Xiao, Wen; Chen, Haiyong; Yang, Peng

doi:10.23919/iconac.2019.8895110

Cited by 46 publications

(35 citation statements)

References 12 publications

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“…Commonly used image reconstruction loss functions including L2 loss [43], mean squared error [44], SSIM [45], etc., belong to the methods that directly compare the images before and after reconstruction. It is also possible to perform defect detection based on differences between the distributions of features of defect-free and defect samples using methods such as GAN models proposed by Schlegl et al-AnoGAN [18] and f-AnoGAN [19], support vector machine classifiers proposed by Liu et al [46] for defect classification, and Ganomaly [20] proposed by Akçay et al…”

Section: Model Testmentioning

confidence: 99%

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images

Wang

Zhang

et al. 2021

Sensors

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Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At present, detection of defective tires is inefficient because tire factories commonly conduct detection by manually checking X-ray images. With the development of deep learning, supervised learning has been introduced to replace human resources. However, in actual industrial scenes, defective samples are rare in comparison to defect-free samples. The quantity of defective samples is insufficient for supervised models to extract features and identify nonconforming products from qualified ones. To address these problems, we propose an unsupervised approach, using no labeled defect samples for training. Moreover, we introduce an augmented reconstruction method and a self-supervised training strategy. The approach is based on the idea of reconstruction. In the training phase, only defect-free samples are used for training the model and updating memory items in the memory module, so the reproduced images in the test phase are bound to resemble defect-free images. The reconstruction residual is utilized to detect defects. The introduction of self-supervised training strategy further strengthens the reconstruction residual to improve detection performance. The proposed method is experimentally proved to be effective. The Area Under Curve (AUC) on a tire X-ray dataset reaches 0.873, so the proposed method is promising for application.

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Section: Model Testmentioning

confidence: 99%

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images

Wang

Zhang

et al. 2021

Sensors

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“…These defects reveal themselves as irregularities on metal or wood surfaces, electronic parts, and so on. For example, an application of visual surface defect detection is studied in four primary studies [33,44,124,69] and industrial quality inspection is investigated in three studies [40,77,99].…”

Section: Rq2: What Are the Application Domains Of Anomaly Detection W...mentioning

confidence: 99%

“…We found that 27 out of 128 primary studies evaluated the quality of the generated samples using 9 different performance evaluation metrics. Most studies evaluated data quality quantitatively, while six papers implemented visual inspection to evaluate the quality of the generated samples [17,40,60,69,139,135]. During the inspections, the generated samples were examined by application domain experts, or simply the authors of the individual studies.…”

Section: Rq4: Which Type Of Data Instance and Datasets Are Most Commo...mentioning

confidence: 99%

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou²,

Bezemer³

et al. 2021

Preprint

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Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area.

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“…However, if images are augmented at various angles for various types of fabrics, the learning time increases proportionally. In addition, this study assumes that learning for fabric vision inspection is processed only with normal data in actual environments as described in [4,5] where it is difficult to obtain abnormal samples for each new-patterned fabric. For this reason, there is a possibility that it is judged as a defect but not a defect because it learns the various unit shapes generated by the sliding window during augmentation as normal even though there is an error with a different pattern area as shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Rotation Estimation and Segmentation for Patterned Image Vision Inspection

Kim²,

Cho

2021

Electronics

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Pattern images can be segmented in a template unit for efficient fabric vision inspection; however, segmentation criteria critically affect the segmentation and defect detection performance. To get the undistorted criteria for rotated images, rotation estimation of absolute angle needs to be proceeded. Given that conventional rotation estimations do not satisfy both rotation errors and computation times, patterned fabric defects are detected using manual visual methods. To solve these problems, this study proposes the application of segmentation reference point candidate (SRPC), generated based on a Euclidean distance map (EDM). SRPC is used to not only extract criteria points but also estimate rotation angle. The rotation angle is predicted using the orientation vector of SRPC instead of all pixels to reduce estimation times. SRPC-based image segmentation increases the robustness against the rotation angle and defects. The separation distance value for SRPC area distinction is calculated automatically. The performance of the proposed method is similar to state-of-the-art rotation estimation methods, with a suitable inspection time in actual operations for patterned fabric. The similarity between the segmented images is better than conventional methods. The proposed method extends the target of vision inspection on plane fabric to checked or striped pattern.

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Steel Surface Defect Detection Using GAN and One-Class Classifier

Cited by 46 publications

References 12 publications

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Rotation Estimation and Segmentation for Patterned Image Vision Inspection

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