从模拟到真实扫描：基于缪子散射断层成像的海运货物异常检测
From Simulation to Real Scans: Anomaly Detection in Maritime Cargo with Muon Scattering Tomography
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中文总结 AI 辅助
本文提出首个海运缪子散射断层成像端到端异常检测框架，通过注意力U-Net和均匀性指数算法，在模拟数据上训练后可弥合模拟到真实的差距，在真实海运货物扫描中实现异常检测。
中文摘要 AI 辅助
海运货物检查需要能够检测致密密封集装箱内隐藏威胁的成像技术，缪子散射断层成像（Muon Scattering Tomography, MST）非常适合这一角色：它利用天然宇宙缪子与物质相互作用产生的、与密度相关的偏转来对集装箱内部成像。然而，MST仍受限于标注扫描数据稀缺，以及宇宙缪子通量低且随机的问题。因此，异常检测算法必须在模拟数据上训练，却要在不同条件下获取的实测扫描数据上运行，这种“模拟到真实”的差距仍是其投入实际部署的核心障碍。本文提出了首个用于海运MST的端到端异常检测框架，涵盖从物理一致性模拟到基于SilentBorder演示活动真实集装箱扫描数据的验证。该任务被视为分布外问题：框架学习良性货物的空间配置，并将威胁标记为重建误差空间中的偏差，对威胁类型和几何形状保持无关性。仅在良性合成场景上训练的注意力U-Net通过其跳跃连接保留小尺度散射特征，因此违禁品会保留在逐像素重建误差中，而非被吸收到重建背景中。评分函数“均匀性指数（Homogeneity Index, HI）”可抑制空间均匀的宇宙射线统计噪声，同时放大相干异常特征：当货物配置变化时，像素级指标会失效，而HI仍保持其判别能力。我们在两种不同货物配置下，按实际一小时扫描时长评估了三种训练策略，并在真实缪子货物扫描数据上测试了最优模型。研究场景的结果表明，“模拟到真实”的差距可以被弥合。
英文摘要
Maritime cargo inspection requires imaging technologies capable of detecting concealed threats within dense, sealed containers, a role for which Muon Scattering Tomography (MST) is well suited: it images their interior through the density-dependent deflection of naturally occurring cosmic muons. However, MST remains constrained by the scarcity of labeled scans and by a cosmic muon flux that is both low and stochastic. Anomaly detection algorithms must therefore be trained on simulations, yet operate on measured scans acquired under different conditions, a sim-to-real gap that remains a central obstacle to operational deployment. We present the first end-to-end anomaly detection framework for maritime MST, from physically consistent simulations to validation on real container scans from the SilentBorder demonstration campaign. The task is cast as an out-of-distribution problem: the framework learns the spatial configurations of benign cargo and flags threats as deviations in the reconstruction error space, remaining agnostic to threat type and geometry. An attention U-Net, trained exclusively on benign synthetic scenes, preserves small-scale scattering signatures through its skip connections, and contraband consequently persists in the pixel-wise reconstruction error instead of being absorbed into the reconstructed background. A scoring function, the Homogeneity Index (HI), suppresses spatially uniform cosmic-ray statistical noise while amplifying coherent anomaly signatures: where pixel-level metrics collapse under a change of cargo configuration, HI retains its discriminative power. We evaluate three training strategies across two distinct cargo configurations under operational one-hour scan times, and test the best model on real muon cargo scans. The results for the studied scenarios indicate that the sim-to-real gap can be bridged.
