Region- and Strength-Controllable GAN for Defect Generation and Segmentation in Industrial Images

Niu, Shuanlong; Li, Bin; Wang, Xinggang; Peng, Yaru

doi:10.1109/tii.2021.3127188

Cited by 53 publications

(17 citation statements)

References 23 publications

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“…The Swin Transformer decoder is a modified version of the transformer decoder that is designed to handle images with high resolution. Shuanlong et al [ 34 ] proposed a novel approach for generating synthetic defects in metal surfaces that is based on the concept of image inpainting. The proposed method regards defect generation as a form of image inpainting, where defects are generated in nondefect images in regions specified by defect masks.…”

Section: Related Workmentioning

confidence: 99%

Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks

Sampath

Maurtua

Martín

et al. 2023

Sensors

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Surface defect identification based on computer vision algorithms often leads to inadequate generalization ability due to large intraclass variation. Diversity in lighting conditions, noise components, defect size, shape, and position make the problem challenging. To solve the problem, this paper develops a pixel-level image augmentation method that is based on image-to-image translation with generative adversarial neural networks (GANs) conditioned on fine-grained labels. The GAN model proposed in this work, referred to as Magna-Defect-GAN, is capable of taking control of the image generation process and producing image samples that are highly realistic in terms of variations. Firstly, the surface defect dataset based on the magnetic particle inspection (MPI) method is acquired in a controlled environment. Then, the Magna-Defect-GAN model is trained, and new synthetic image samples with large intraclass variations are generated. These synthetic image samples artificially inflate the training dataset size in terms of intraclass diversity. Finally, the enlarged dataset is used to train a defect identification model. Experimental results demonstrate that the Magna-Defect-GAN model can generate realistic and high-resolution surface defect images up to the resolution of 512 × 512 in a controlled manner. We also show that this augmentation method can boost accuracy and be easily adapted to any other surface defect identification models.

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Section: Related Workmentioning

confidence: 99%

Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks

Sampath

Maurtua

Martín

et al. 2023

Sensors

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“…Cai et al [6] innovatively proposed a framework for transfer reinforcement learning for the reconstruction of multiview optical fields. Niu et al [7] proposed a defect image generation method with controllable defect area and intensity. The generated defect area was controlled by using a defect mask.…”

Section: Related Workmentioning

confidence: 99%

Underwater Incomplete Target Recognition Network via Generating Feature Module

Shen

Jia

Cai

2023

International Journal of Distributed Sensor Networks

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A complex and changeable underwater archaeological environment leads to the lack of target features in the collected images, affecting the accuracy of target detection. Meanwhile, the difficulty in obtaining underwater archaeological images leads to less training data, resulting in poor generalization performance of the recognition algorithm. For these practical issues, we propose an underwater incomplete target recognition network via generating feature module (UITRNet). Specifically, for targets that lack features, features are generated by dual discriminators and generators to improve target detection accuracy. Then, multilayer features are fused to extract regions of interest. Finally, supervised contrastive learning is introduced into few-shot learning to improve the intraclass similarity and interclass distance of the target and enhance the generalization of the algorithm. The UIFI dataset is produced to verify the effectiveness of the algorithm in this paper. The experimental results show that the mean average precision (mAP) of our algorithm was improved by 0.86% and 1.29% under insufficient light and semiburied interference, respectively. The mAP for ship identification reached the highest level under all four sets of experiments.

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“…Li et al [ 27 ] proposed a depth translation-based change detection network (DTCDN) for optical and SAR images, using generative adversarial networks to transform the domains of anomalous remote sensing images to achieve remote sensing image change difference detection. Niu et al [ 28 ] constructed defect direction vectors in the potential space of GAN networks to control the defect intensity based on the feature continuity between defects and nondefects in industrial images to achieve defect detection. Most of the anomaly detection methods based on generative adversarial networks detect anomalies by inverse mapping the samples to be tested back to the latent space and reconstructing the samples using generators, while there are also methods that use discriminators and generators together to detect anomalies.…”

Section: Related Workmentioning

confidence: 99%

Research on DUAL-ADGAN Model for Anomaly Detection Method in Time-Series Data

Gong

Wang

2022

Computational Intelligence and Neuroscience

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In recent years, anomaly detection techniques in time-series data have been widely used in manufacturing, cybersecurity, and other fields. Meanwhile, various anomaly detection models based on generative adversarial networks (GAN) are gradually used in time-series anomaly detection tasks. However, there are problems of unstable generator training, missed detection of anomalous data, and inconsistency between the discriminator’s discriminant and the anomaly detection target in GAN networks. Aiming at the above problems, the paper proposes a DUAL-ADGAN (Dual Anomaly Detection Generative Adversarial Networks) model for the detection of anomalous data in time series. First, the Wasserstein distance satisfying the Lipschitz constraint is used as the loss function of the data reconstruction module, which improves the stability of the traditional GAN network training. Second, by adding a data prediction module to the DUAL-ADGAN model, the distinction between abnormal and normal samples is increased, and the rate of missing abnormal data in the model is reduced. Third, by introducing the Fence-GAN loss function, the discriminator is aligned with the anomaly detection target, which effectively reduces the anomaly data false detection rate of the DUAL-ADGAN model. Finally, anomaly scores derived from the DUAL-ADGAN model are compared with dynamic thresholds to detect anomalies. The experimental results show that the average F1 of the DUAL-ADGAN model is 0.881, which is better than the other nine baseline models. The conclusions demonstrate that the DUAL-ADGAN model proposed in the paper is more stable in training while effectively solving the problems of anomaly miss detection and discriminator inconsistency with the anomaly detection target in the anomaly detection task.

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Region- and Strength-Controllable GAN for Defect Generation and Segmentation in Industrial Images

Cited by 53 publications

References 23 publications

Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks

Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks

Underwater Incomplete Target Recognition Network via Generating Feature Module

Research on DUAL-ADGAN Model for Anomaly Detection Method in Time-Series Data

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