LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing

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.

Cross-Modal Distillation in Industrial Anomaly Detection: Exploring Efficient Multi-Modal IAD

Recent studies of multi-modal Industrial Anomaly Detection (IAD) based on point clouds and RGB images indicated the importance of exploiting redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multi-modal IAD in practical production lines remains a work in progress that requires consideration of the trade-offs between costs and benefits associated with introducing new modalities, while ensuring compatibility with current processes. Combining fast in-line inspections with high-resolution, time-consuming, near-line characterization techniques to enhance detection accuracy fits well into the existing quality control process, but only part of the samples can be tested with expensive near-line methods. Thus, the model must have the ability to leverage multi-modal training and handle incomplete modalities during inference. One solution is generating cross-modal hallucination to transfer knowledge among modalities for missing modality issues. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of Multi-modal Training, Few-modal Inference pipeline. Moreover, we investigate reasons behind the asymmetric performance improvement using point clouds or RGB images as main modality of inference. This lays the foundation of our future multi-modal dataset construction for efficient IAD from manufacturing scenarios.