Deep industrial image anomaly detection: A survey
The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …
Collaborative discrepancy optimization for reliable image anomaly localization
Most unsupervised image anomaly localization methods suffer from overgeneralization
because of the high generalization abilities of convolutional neural networks, leading to …
because of the high generalization abilities of convolutional neural networks, leading to …
RealNet: A feature selection network with realistic synthetic anomaly for anomaly detection
Self-supervised feature reconstruction methods have shown promising advances in
industrial image anomaly detection and localization. Despite this progress these methods …
industrial image anomaly detection and localization. Despite this progress these methods …
A new foreground-perception cycle-consistent adversarial network for surface defect detection with limited high-noise samples
Y Wang, W Hu, L Wen, L Gao - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
Surface defect detection (SDD) is critical in the smart manufacturing systems to ensure
product quality. Nevertheless, the defective samples are always insufficient, and there exists …
product quality. Nevertheless, the defective samples are always insufficient, and there exists …
Global prior transformer network in intelligent borescope inspection for surface damage detection of aeroengine blade
Surface damage detection is vital for diagnosis and monitoring of aeroengine blade. At
present, borescope inspection is the dominant technology. Several inspectors hold …
present, borescope inspection is the dominant technology. Several inspectors hold …
Bias: Incorporating biased knowledge to boost unsupervised image anomaly localization
Image anomaly localization is a pivotal technique in industrial inspection, often manifesting
as a supervised task where abundant normal samples coexist with rare abnormal samples …
as a supervised task where abundant normal samples coexist with rare abnormal samples …
Dual-attention transformer and discriminative flow for industrial visual anomaly detection
In this paper, we introduce the novel state-of-the-art Dual-attention Transformer and
Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal …
Discriminative Flow (DADF) framework for visual anomaly detection. Based on only normal …
Anomaly detection using siamese network with attention mechanism for few-shot learning
H Takimoto, J Seki, S F. Situju… - Applied Artificial …, 2022 - Taylor & Francis
Automated inspection using deep-learning has been attracting attention for visual inspection
at the manufacturing site. However, the inability to obtain sufficient abnormal product data for …
at the manufacturing site. However, the inability to obtain sufficient abnormal product data for …
An unsupervised spatiotemporal fusion network augmented with random mask and time-relative information modulation for anomaly detection of machines with …
K Zhang, J Chen, CG Lee, S He - Expert Systems with Applications, 2024 - Elsevier
In industrial environments, individual sensor is easily affected by background noise, etc. In
order to improve the reliability of anomaly detections, sensors are arranged at multiple …
order to improve the reliability of anomaly detections, sensors are arranged at multiple …
Surface defect detection method for air rudder based on positive samples
Z Yang, M Zhang, Y Chen, N Hu, L Gao, L Liu… - Journal of Intelligent …, 2024 - Springer
In actual industrial applications, the defect detection performance of deep learning models
mainly depends on the size and quality of training samples. However, defective samples are …
mainly depends on the size and quality of training samples. However, defective samples are …