重新思考边缘上的持续异常检测：在现实工业条件下进行基准测试
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
AI总结：
针对现有持续异常检测方法在评估、比较和边缘部署约束上的不足，提出统一基准和训练无关方法DINOSaur，在多种协议下超越所有现有方法，并在边缘设备上实现快速推理和适应。
摘要：
持续异常检测（CAD）解决了工业检测系统适应不断变化的生产条件的需求，但现有方法存在三个关键差距：不现实的评估、缺乏系统比较以及未考虑边缘部署约束。我们引入了一个统一的基准，结合了结构和逻辑异常的离散任务评估、一种新颖的连续漂移协议、对所有已发布CAD方法的首次头对头比较，以及在边缘硬件上的计算效率分析。我们的结果表明，现有的CAD方法并不一致地优于带有简单经验重放的传统方法。受此启发，我们提出了DINOSaur，一种无需训练的方法，结合了冻结的DINOv3骨干网络、空间索引的coreset记忆和邻域限制的异常评分。DINOSaur通过构造实现了零遗忘，在所有五种协议上优于所有评估的方法，并在NVIDIA Jetson Orin Nano上以低于100毫秒的推理速度运行，在设备上适应新任务的时间不到30秒。
英文摘要：
Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods share three critical gaps: unrealistic evaluation, no systematic comparison, and no consideration of edge deployment constraints. We introduce a unified benchmark combining discrete-task evaluation on structural and logical anomalies, a novel continuous drift protocol, the first head-to-head comparison of all published CAD methods, and computational efficiency profiling on edge hardware. Our results reveal that existing CAD methods do not consistently outperform traditional approaches with simple experience replay. Thus motivated, we propose DINOSaur, a training-free method combining a frozen DINOv3 backbone with spatially-indexed coreset memory and neighborhood-restricted anomaly scoring. DINOSaur achieves zero forgetting by construction, outperforms all evaluated methods across all five protocols, and runs at sub-100\,ms inference on an NVIDIA Jetson Orin Nano, with on-device adaptation to new tasks in under 30 seconds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
