LIBAD：面向锂离子电池电极制造的多模态异常检测基准
LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing
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中文总结 AI 辅助
本文推出首个面向锂离子电池电极制造的多模态异常检测基准LIBAD，提出DA-Core方法，其在核心集比例0.05时可降低误报率并缩短推理时间，优于相关基准方法。
中文摘要 AI 辅助
多模态工业异常检测大多聚焦于使用强相关RGB与3D观测的离散产品，而连续流程制造与弱相关传感模态的相关研究仍不充分。本文推出LIBAD，首个面向锂离子电池电极制造的多模态异常检测基准。LIBAD采集自实际卷对卷生产线，提供对齐的双侧可见光成像、高分辨率X射线射线照相，以及兼容在线检测的低分辨率X射线射线照相。LIBAD中的电极片呈现高度均匀的材料外观，而缺陷证据可能在某一模态中明显，但在另一模态中微弱或缺失，导致显著的跨模态异常不一致性。对兼容在线检测的可见光与低分辨率X射线场景下的代表性方法进行基准测试后发现，这些方法的迁移能力有限，且始终存在较高的误报率（FPR）。因此，本文提出DA-Core，一种基于记忆的方法，该方法在核心集选择期间同时考虑特征空间覆盖范围与正常特征的局部密度，使紧凑的记忆库能更好地保留细粒度的正常变化。当核心集比例为0.05时，与标准最远点采样相比，DA-Core将FPR95从60.4%降至54.3%；在该比例下，DA-Core还优于在0.20比例下获得的最佳标准核心集结果，同时将推理时间减少43.9%。这些结果表明，在设计流程制造用异常检测方法时，需明确考虑正常特征的数据分布以及模态关系本身。
英文摘要
Multimodal industrial anomaly detection has largely focused on discrete products using strongly correlated RGB and 3D observations, leaving continuous process manufacturing and weakly correlated sensing modalities underexplored. We introduce LIBAD, the first multimodal anomaly detection benchmark for Li-ion battery electrode manufacturing. Collected from real roll-to-roll production lines, LIBAD provides aligned double-sided visible-light imaging, high-resolution X-ray radiography, and inline-compatible low-resolution X-ray radiography. Electrode patches in LIBAD exhibit highly homogeneous material appearance, while defect evidence can be strong in one modality but weak or absent in another, resulting in pronounced cross-modal anomaly inconsistency. Benchmarks of representative methods under the inline-compatible visible-light and low-resolution X-ray setting exhibit limited transferability and consistently high false-positive rates. We therefore propose DA-Core, a memory-based method that jointly considers feature-space coverage and local density of normal features during coreset selection, allowing compact memory banks to better preserve fine-grained normal variations. With a coreset ratio of 0.05, DA-Core reduces FPR95 from 60.4% to 54.3% compared with standard farthest point sampling. At this ratio, DA-Core also outperforms the best standard coreset result (obtained at 0.20) while reducing inference time by 43.9%. These results suggest that both the data distribution of normal features and the modality relationship itself require explicit consideration when designing anomaly detection methods for process manufacturing.
