Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions

Continual anomaly detection (CAD) addresses the need forindustrial inspection systems to adapt to evolving production conditions,yet existing methods share three critical gaps: unrealistic evaluation, nosystematic comparison, and no consideration of edge deployment con-straints. We introduce a unified benchmark combining discrete-task eval-uation on structural and logical anomalies, a novel continuous drift proto-col, the first head-to-head comparison of all published CAD methods, andcomputational efficiency profiling on edge hardware. Our results revealthat existing CAD methods do not consistently outperform traditionalapproaches with simple experience replay. Thus motivated, we proposeDINOSaur, a training-free method combining a frozen DINOv3 back-bone with spatially-indexed coreset memory and neighborhood-restrictedanomaly scoring. DINOSaur achieves zero forgetting by construction,outperforms all evaluated methods on every protocol except geometricdrift (where all methods collapse to chance), and runs at sub-100 ms in-ference on an NVIDIA Jetson Orin Nano, with on-device adaptation tonew tasks in under 30 seconds.