arXiv Computer Vision
Sep 7

Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion

The paper introduces a training‑free anomaly detector that simultaneously handles structural and logical defects by calibrating heterogeneous anomaly cues with statistics from normal images. This calibration aligns frozen representations, allowing their fusion without extra training or part‑level supervision. The resulting method achieves state‑of‑the‑art AUROC scores on MVTec‑LOCO and remains competitive on MVTec‑AD.

By Changyi Li, Miao Yu, Kai Dong, Yu Xiao
arXiv Computer Vision
Sep 4

ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects

The paper introduces ISP-AD, the largest publicly available industrial anomaly detection dataset, featuring both synthetic and real defects from a factory floor. It focuses on challenging, small, weakly contrasted surface defects within highly variable structured patterns, addressing the bias of existing datasets toward ideal imaging conditions. Experiments demonstrate that even a small amount of weakly labeled real defects improves model generalization and that synthetic defects can serve as a useful cold‑start baseline for scalable training.

By Paul J. Krassnig, Dieter P. Gruber
arXiv Computer Vision
1d ago

Robust Online Aero-Engine Blade Defect Detection via Dual-Alignment Test-Time Adaptation

The paper introduces the Aero-engine Blade Defect Detector (ABDD), an online adaptive detection framework that uses test-time adaptation to handle domain shifts in aero-engine blade inspection. ABDD employs a Dual-Alignment Strategy combining feature-statistics alignment with pseudo-box alignment to adapt both global visual style and local defect morphology, and incorporates an Uncertainty-aware Box Filtering mechanism to mitigate errors from noisy pseudo labels. A lightweight Sparse Dilated Mona module enables efficient parameter tuning while preventing source-domain forgetting, and the method is validated on CD-AeBD and HD-AeBD datasets, showing improved robustness and practical applicability on an industrial inspection platform.

By Zhaoyang Wang, Haiyong Chen, Dongying Li, Yining Wang, Huapeng Wu, Xinwei Lv, Atik Shahariar
arXiv Machine Learning
Aug 11

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection

arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.

By Mike Szklarzewski, CJ George, Gavin Smithson, Christopher Stokes, Dakota Fulp, William M. Jones, Benjamin Wynn, Alexander Ur, Agit Yesiloz, Clint Kallenbach, Mark Swartz, Nathan DeBardeleben, Sharmistha Chakrabarti