当前位置: X-MOL 学术Phys. Rev. Research › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Determining urban material activities with a vehicle-based multi-sensor system
Physical Review Research ( IF 3.5 ) Pub Date : 2021-04-23 , DOI: 10.1103/physrevresearch.3.023070
M. Salathe , B. J. Quiter , M. S. Bandstra , J. C. Curtis , R. Meyer , C. H. Chow

Integration of contextual sensors into vehicle-borne mobile radiation detection systems delivers a rich description of the environment to inform estimates of the complex and variable gamma-ray signals observed in urban areas. Models based on these data streams could provide realistic inputs to urban radiological search algorithms and potentially improve the system's sensitivity to detect illicit radiological and nuclear materials. In this work, LiDAR and inertial data are combined using simultaneous localization and mapping techniques to create a three-dimensional (3D) representation of the surrounding scenery. Semantic segmentation of concurrently collected video imagery enables the division of the 3D model into distinct material categories. The radioactive flux of surfaces associated with these categories are inferred through maximum likelihood estimation maximization and the activity of the three most common isotopes (K-40, U-238 series, and Th-232 series) in the respective materials is predicted. The results, found to be in agreement with ground truth measurements performed at the facility, suggest that it is possible to quickly infer the composition of naturally occurring materials in structures that comprise a radiological scene. Such a capability could be used to inform radiological search algorithms and enable data-driven modeling of radiological search problems, which could facilitate system testing and operator training activities.

中文翻译:

使用基于车辆的多传感器系统确定城市物质活动

将上下文传感器集成到车载移动辐射检测系统中,可以对环境进行丰富的描述,以告知对在城市地区观察到的复杂且可变的伽马射线信号的估计。基于这些数据流的模型可以为城市放射学搜索算法提供现实的输入,并有可能提高系统检测非法放射学和核材料的敏感性。在这项工作中,使用同时定位和制图技术将LiDAR和惯性数据结合起来,以创建周围风景的三维(3D)表示。同时收集的视频图像的语义分割可将3D模型划分为不同的材料类别。通过最大似然估计最大化来推断与这些类别相关的表面的放射性通量,并预测了相应材料中三种最常见的同位素(K-40,U-238系列和Th-232系列)的活度。发现的结果与在该设施进行的地面真相测量相符,表明可以快速推断出构成放射线场景的结构中自然存在的物质的成分。这种功能可用于通知放射学搜索算法,并能对放射学搜索问题进行数据驱动的建模,从而有助于系统测试和操作员培训活动。和Th-232系列)在相应材料中的预测。发现的结果与在该设施进行的地面真相测量相符,表明可以快速推断出构成放射线场景的结构中自然存在的物质的成分。这种功能可用于通知放射学搜索算法,并能对放射学搜索问题进行数据驱动的建模,从而有助于系统测试和操作员培训活动。和Th-232系列)在相应材料中的预测。发现的结果与在该设施进行的地面真相测量相符,表明可以快速推断出构成放射线场景的结构中自然存在的物质的成分。这种功能可用于通知放射学搜索算法,并能对放射学搜索问题进行数据驱动的建模,从而有助于系统测试和操作员培训活动。
更新日期:2021-04-23
down
wechat
bug