面向多模态在线分布式工业异常检测的参数高效多类智能调度
Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
AI总结：
针对工业异常检测中分布式、持续生成数据的特点，提出多模态在线分布式工业异常检测框架，通过多类智能调度问题和序列边际增益贪婪算法协调模型更新，并采用资源高效类级低秩适应策略降低系统开销，在MVTec 3D-AD和Eyecandies数据集上取得优越性能。
摘要：
工业异常检测作为工业系统的基本挑战已引起广泛关注。异构工业传感器的快速发展推动工业异常检测从单模态向多模态范式转变。然而，现有方法主要针对集中式和离线场景设计，忽视了实际工业环境中分布式和持续生成的数据特征。随着边缘智能的发展，现代边缘设备不仅能够采集数据，还能进行分布式模型训练，实现系统范围内的协作智能。工业异常检测是此背景下的关键应用。受这些挑战启发，我们提出了一种名为多模态在线分布式工业异常检测（MODIAD）的新框架。首先给出了MODIAD的完整工作流程，然后制定了多类智能调度（MIS）问题，通过平衡数据充足性和类别更新频率来协调跨类模型更新。为了高效解决该问题，我们设计了序列边际增益贪婪（SMG）算法，能够在资源约束下实现有效的多类训练。此外，为了提升训练过程中的计算和通信效率，我们提出了资源高效类级低秩适应（REC-LoRA）策略，在保持检测性能的同时显著降低系统开销。在两个代表性多模态工业异常检测数据集MVTec 3D-AD和Eyecandies上的大量实验表明，所提方法在MODIAD场景下实现了优越的性能和效率。
英文摘要：
Industrial anomaly detection has attracted significant attention as a fundamental challenge in industrial systems. The rapid advancement of heterogeneous industrial sensors has driven industrial anomaly detection from unimodal to multimodal paradigms. However, existing methods are primarily designed for centralized and offline settings, overlooking the distributed and continuously generated data characteristic of real-world industrial environments. With the advancement of edge intelligence, modern edge devices are increasingly capable of not only data acquisition but also distributed model training, enabling collaborative intelligence across the system. Industrial anomaly detection represents a critical application in this context. Motivated by these challenges, we propose a novel framework termed Multimodal Online Distributed Industrial Anomaly Detection (MODIAD). We first present a comprehensive workflow for MODIAD and then formulate a Multi-class Intelligent Scheduling (MIS) problem to coordinate cross class model updates by balancing data sufficiency and class update frequency. To efficiently solve this problem, we design a Sequential Marginal Gain Greedy (SMG) algorithm that enables effective multi-class training under resource constraints. Furthermore, to improve the computational and communication efficiency during training, we propose an Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy, which significantly reduces system overhead while preserving detection performance. Extensive experiments on two representative multimodal industrial anomaly detection datasets, MVTec 3D-AD and Eyecandies demonstrate that the proposed approach achieves superior performance and efficiency under the MODIAD scenario.
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