This paper introduces the first deployment-aware benchmarking protocol for IAD, bridging the gap between academic performance and practical industrial requirements, and highlights the limitations of current benchmark practices.
A novel large multimodal model applying vision experts for industrial anomaly detection (abbreviated as Myriad), which treats conventional IAD models as VEs and converts their anomaly maps into lightweight prompts that steer a frozen Q-Former toward suspicious regions, while a compact low-rank adapter shapes features f...
Yuanze Li, Haolin Wang, Shihao Yuan et al.· Science China Information Sc...· 0 citations
Few-shot anomaly detectors for industrial inspection typically use between five and 20 reference images to learn what a normal part looks like. These images are assumed to be defect-free, which is not 100% guaranteed on an actual production line. This makes the reference set a potential point of failure: if an image of...
Sergio Villanueva López, E. Soria-Olivas, Manuel A. Sánchez-Montañés· Electronics· 0 citations
Experimental results demonstrate that the proposed method for industrial defect detection and localization based on small-sized objects shows significant performance improvement over related approaches in binary classification tasks.
Xiaosha Qi, Li-Zhu Yang, Lin Yang et al.· Journal of King Saud Univers...· 0 citations
Automated visual quality inspection often operates with few normal referenceimages but still requires an explicit policy for asymmetric operational errors. This paperpresents Risk-Calibrated Few-Shot Industrial Anomaly Detection Plus (RC-FS-IAD+), afew-normal-support, labeled-calibration-assisted framework that maps an...
Ismail Hakki Kinalioglu· Journal of Advanced Research...· 0 citations
Automated quality inspection of painted metal parts requires robust visual anomaly detection (VAD) capable of identifying defects despite limited training data and unpredictable anomalies. Supervised algorithms are often impractical due to the limited availability of labeled anomaly data, yet existing unsupervised and...
S. Jezek, Martin Jonák, Milos Skotak et al.· IEEE Access· 0 citations
Point cloud anomaly detection, aimed at detecting subtle geometric deviations, has emerged as a critical task in quality control. While existing methods predominantly employ prototype-based frameworks that quantify anomalies via normal prototypical distances, we identify a fundamental limitation: such approaches neglec...
Yu-qi Cheng, Yun-Kang Cao, Haiming Yao et al.· IEEE Transactions on Automat...· 0 citations
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