提出首个面向工业边缘部署的持续异常检测基准与高效方法。
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
- 构建包含真实工业场景的统一评估基准，涵盖结构与逻辑异常
- 新方法DINOSaur零遗忘，推理<100毫秒，30秒内完成设备端任务更新
- 首次系统对比所有已有方法，发现传统经验回放仍具竞争力
持续异常检测（CAD）旨在使工业检测系统适应生产环境变化，但现有方法存在三大缺陷：评估不真实、缺乏系统性比较、未考虑边缘部署限制。本文提出统一基准，包含结构与逻辑异常的离散任务评估、新型连续漂移协议、所有已发表CAD方法的首次直接对比，以及在边缘硬件上的计算效率分析。结果表明，现有方法并未始终优于采用简单经验回放的传统方法。受此启发，提出DINOSaur：一种无需训练的方法，结合冻结的DINOv3主干网络、空间索引的核样本记忆库和邻域受限异常评分机制。该方法天然实现零遗忘，在全部五个评估协议中超越所有被测方法，并在NVIDIA Jetson Orin Nano上实现<100毫秒推理，设备端新任务适应时间低于30秒。
原文摘要 · Abstract (English)
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.
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