GuidedFlow：一种用于增材制造异常检测的注意力引导框架
GuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturing
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中文总结 AI 辅助
针对增材制造中微小/拉丝缺陷的检测难题，提出注意力引导归一化流模型GuidedFlow，在AM3D-AD和MVTec-AD数据集上验证其性能优于多数现有模型，提升了检测准确率与AUROC。
中文摘要 AI 辅助
增材制造（AM）在当前工业革命中发挥着至关重要的作用。然而，由于打印缺陷或潜在的网络物理入侵，质量控制仍然是关键且具有挑战性的问题。基于图像或视频的异常检测是应对这些挑战的关键举措。该领域已探索了多种方法，包括基于重建、嵌入和流的方法。尽管基于归一化流的方法解决了意外缺陷和泛化的一些核心挑战，同时保持了检测性能，但现有方法难以处理3D打印中常见的微小/拉丝缺陷，在小数据设置下，这对泛化构成了限制。为解决这些限制，我们提出了**GuidedFlow**，一种用于异常检测和定位的新型注意力引导归一化流模型。GuidedFlow采用在领域数据集上微调的预训练ResNet模型。注意力引导的时空流框架对多尺度和多帧的动态进行建模。时空注意力网络（SAN）使流模型能够优先考虑输入帧中相关的上下文线索。我们在AM3D-AD数据集上评估GuidedFlow，该数据集包含良性和异常的真实3D打印物体图像和视频。我们还使用MVTec-AD工业图像异常检测数据集进行了比较研究。实验结果表明，GuidedFlow在提高检测准确率和AUROC的情况下，优于大多数最先进的模型。
英文摘要
Additive Manufacturing (AM) plays a vital role in the ongoing industrial revolution. However, quality control remains crucial and challenging due to printing defects or potential cyber-physical intrusions. Image or video-based anomaly detection is a key effort towards addressing these challenges. Various approaches have been explored in this domain, including reconstruction-based, embedding-based, and flow-based methods. Though normalizing flow-based methods address some of the core challenges of unforeseen defects and generalization while maintaining detection performance, existing approaches struggle with tiny/stringing defects common in 3D printing. In a small-data setting, this poses a limitation in generalization. To address these limitations, we propose \textbf{GuidedFlow}, a novel attention-guided normalizing flow model for anomaly detection and localization. GuidedFlow employs a pre-trained ResNet model, fine-tuned on the domain dataset. An attention-guided spatial and temporal flow framework models the dynamics across multiple scales and frames. A Spatio-Temporal Attention Network (SAN) enables the flow model to prioritize relevant contextual cues from input frames. We evaluate GuidedFlow on our AM3D-AD dataset, consisting of benign and anomalous real 3D printed object images and videos. We also conduct a comparative study using the MVTec-AD industrial image anomaly detection dataset. Experimental results demonstrate that GuidedFlow outperforms most of the state-of-the-art models with enhanced detection accuracy and AUROC.
发表机构
- New Mexico Institute of Mining and Technology（新墨西哥矿业技术学院）
机构由 AI 辅助整理，请以论文原文为准。
