不完整多模态工业异常检测：基于跨模态蒸馏的方法
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillation
AI总结：
针对实际生产线中多模态数据不完整的问题，提出跨模态蒸馏框架CMDIAD，实现多模态训练、少模态推理，有效利用不完整信息提升异常检测性能。
摘要：
基于3D点云和RGB图像的多模态工业异常检测（IAD）研究近期强调了利用模态间的冗余性和互补性对于准确分类和分割的重要性。然而，在实际生产线中实现多模态IAD仍是一个正在进行的工作。必须考虑引入新模态所带来的成本与收益之间的权衡，同时确保与当前流程的兼容性。现有的质量控制流程结合了快速在线检测（如光学和红外成像）与高分辨率但耗时的近线表征技术（包括工业CT和电子显微镜），用于在锂离子电池和复合材料的生产中手动或半自动定位和分析缺陷。鉴于成本和时间限制，只有一部分样本能够通过所有在线和近线方法进行检测，其余样本仅通过一种或两种在线检测形式进行评估。为了充分利用数据进行深度学习驱动的自动缺陷检测，模型必须具备利用多模态训练并在推理期间处理不完整模态的能力。在本文中，我们提出了CMDIAD，一种用于IAD的跨模态蒸馏框架，以证明多模态训练、少模态推理（MTFI）流程的可行性。我们的研究结果表明，与仅使用单一模态进行训练和推理相比，MTFI流程能够更有效地利用不完整的多模态信息。此外，我们调查了使用点云或RGB图像作为主要推理模态时性能提升不对称的原因。这为我们未来从制造场景中构建带有额外模态的多模态数据集奠定了基础。
英文摘要：
Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to consider the trade-offs between the costs and benefits associated with the introduction of new modalities while ensuring compatibility with current processes. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques, including industrial CT and electron microscopy to manually or semi-automatically locate and analyze defects in the production of Li-ion batteries and composite materials. Given the cost and time limitations, only a subset of the samples can be inspected by all in-line and near-line methods, and the remaining samples are only evaluated through one or two forms of in-line inspection. To fully exploit data for deep learning-driven automatic defect detection, the models must have the ability to leverage multimodal training and handle incomplete modalities during inference. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference (MTFI) pipeline. Our findings show that the MTFI pipeline can more effectively utilize incomplete multimodal information compared to applying only a single modality for training and inference. Moreover, we investigate the reasons behind the asymmetric performance improvement using point clouds or RGB images as the main modality of inference. This provides a foundation for our future multimodal dataset construction with additional modalities from manufacturing scenarios.
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。
