设备端持续学习用于动态制造中的无监督视觉异常检测
On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing
AI总结：
本文提出了一种设备端持续学习方法，用于动态制造中的无监督视觉异常检测，通过轻量级特征提取器和增量coreset更新机制实现快速适应和高效资源利用。
摘要：
在现代制造中，视觉异常检测（VAD）对于自动化检测和保持产品质量一致性至关重要。然而，日益动态和灵活的生产环境带来了关键挑战：首先，小批量和按需制造中频繁的产品变化要求模型快速更新。其次，遗留的边缘硬件缺乏训练和运行大型AI模型的资源。最后，异常和正常训练数据通常稀缺，尤其是对于新引入的产品变体。我们研究了具有定位功能的设备端持续学习用于无监督VAD，扩展了PatchCore以纳入在线学习以适应现实工业场景。所提出的方法利用轻量级特征提取器和基于k中心选择的增量coreset更新机制，使从有限数据中快速、内存高效地适应，同时消除昂贵的云重新训练成本。在工业用例上进行评估，使用设计用于模拟具有频繁变体变化的灵活生产的测试平台。我们的方法在基准上实现了12%的AUROC提升，内存使用减少了80%，并且比批量重新训练更快。这些结果证实了我们的方法能够提供准确、资源高效且适应性强的VAD，适用于动态和智能制造。
英文摘要：
In modern manufacturing, Visual Anomaly Detection (VAD) is essential for automated inspection and consistent product quality. Yet, increasingly dynamic and flexible production environments introduce key challenges: First, frequent product changes in small-batch and on-demand manufacturing require rapid model updates. Second, legacy edge hardware lacks the resources to train and run large AI models. Finally, both anomalous and normal training data are often scarce, particularly for newly introduced product variations. We investigate on-device continual learning for unsupervised VAD with localization, extending the PatchCore to incorporate online learning for real-world industrial scenarios. The proposed method leverages a lightweight feature extractor and an incremental coreset update mechanism based on k-center selection, enabling rapid, memory-efficient adaptation from limited data while eliminating costly cloud retraining. Evaluations on an industrial use case are conducted using a testbed designed to emulate flexible production with frequent variant changes in a controlled environment. Our method achieves a 12% AUROC improvement over the baseline, an 80% reduction in memory usage, and faster training compared to batch retraining. These results confirm that our method delivers accurate, resource-efficient, and adaptive VAD suitable for dynamic and smart manufacturing.
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。
