Development of 3D Visualisation Algorithms for the Effective Interpretation of Tunnel Subsurface Radar Data

开发 3D 可视化算法以有效解释隧道地下雷达数据

基本信息

批准号：
2440423
负责人：
金额：
--
依托单位：
Newcastle University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2440423
关键词：
Development 3D Visualisation Algorithms Effective

项目摘要

Relevant EPSRC Research Areas:Architectures and operating systems; Artificial intelligence technologies; Digital signal processing; Graphics and visualisation; Ground Engineering; Human-communication interaction; Image and vision computing; Infrastructure and urban systems; Mathematical physics; Numerical Analysis; Operational Research; Programming languages and compilers; Software Engineering; Structural Engineering Project Synopsis:Anticipated growth of global rail network demand is set to further increase pressure on aging tunnel infrastructure. The inherent safety risks, severity of structural compromise and ever reducing allocatable resources for targeted maintenance, has prompted development of TRACKSCAN, an infrastructure inspection hardware solution based on Ground-Penetrating Radar (GPR), able to rapidly produce comprehensive 360-degree point-cloud tunnel structure profiles, referred to here on as 360-GPR data. However, this hardware currently lacks a software counterpart that can clearly convey this data to human operatives. This project proposes the development of bespoke visualisation software to translate raw 360-GPR data from TRACKSCAN into immersive, intuitive, 3D digital survey environments to facilitate non-disruptive off-site inspections of tunnel structural integrity through the use of Virtual Reality (VR) hardware. The main challenge to overcome is that the GPR data concerned is inherently non-planar, but helical, as TRACKSCAN utilises a unique rotary kinematic antenna array. Subsequently, leading algorithms for GPR data processing and visualisation, developed across numerous independently conducted research projects, cannot function on this data. Ergo, the primary research goal of this project is the formulation, implementation, and unification of novel procedural augmentations to these strategies for the development of algorithms optimised to enrich, segment and triangulate subsurface geometry from pioneering 360-GPR data. The secondary challenge of this project is to address the prominent lack of interpretive clarity associated with feature identification in conventional GPR data displays, which has made structural information obtained inaccessible to non-radar trained rail industry operatives. Therefore, visualiser development will introduce a deep learning-based object classification scheme to dynamically highlight and qualitatively assess subsurface features (i.e. assets, defects, and medium characteristics) present within 360-GPR data in real time. Extracted information will then be intuitively conveyed to the end-user by design and integration of a dedicated streamlined user interface. Additionally, this project seeks to improve achievable levels of operative immersion within the survey data, facilitating more ergonomic execution of virtual inspections and permitting unrestricted analysis of structural features onsite teams cannot easily view (e.g. narrow culvert interiors). This will be achieved through the creation of an original rendering engine, capable of translating subsurface geometry interpolated from the 360-GPR data into realistically textured 3D virtual environments. Navigation will utilise a commercial VR hardware unit, placing operatives directly within the digitally reconstructed tunnels, whilst texture realism will stem from the development of new algorithms to fuse 360-GPR data with corresponding near-surface 3D Lidar scans.Research proposed will therefore centre on the Development of 3D Visualisation Algorithms for the Effective Interpretation of Tunnel Subsurface Radar Data generated by the new Infrastructure Inspection Radar system TRACKSCAN. This work will involve software engineering; practical testing using VR hardware; topics in machine learning, computational geometry, and human computer interaction within the gaming industry; alongside investigation of the physics and mathematics behind 360-GPR data inversion.

相关EPSRC研究领域：建筑和操作系统；人工智能技术；数字信号处理；图形和可视化；地面工程；人类交互；图像和视觉计算；基础设施和城市系统；数学物理学；数值分析；运营研究；编程语言和编译器；软件工程；结构工程项目概要：全球铁路网络需求的预期增长将进一步增加对老化隧道基础设施的压力。固有的安全风险，结构性妥协的严重性以及对有针对性维护的可分配资源的发展，促使TrackScan的开发是一种基于地面渗透雷达（GPR）的基础架构检查硬件解决方案（GPR），能够迅速迅速生成360度露头式隧道结构pifiles，以此引用到360-GPR上。但是，该硬件目前缺少可以将这些数据清楚地传达给人类操作员的软件。该项目提出了定制可视化软件的开发，以将RAW 360-GPR数据从TrackScan转换为沉浸式，直觉，3D数字调查环境，以促进通过使用虚拟现实（VR）硬件对隧道结构完整性的非干扰现场检查。要克服的主要挑战是，有关GPR数据本质上是非平面的，但是螺旋式的数据是TrackScan使用独特的旋转运动学天线阵列。随后，在众多独立执行的研究项目中开发的用于GPR数据处理和可视化的领先算法无法在此数据上起作用。 Ergo，该项目的主要研究目标是将新的程序增强的制定，实施和统一，以开发优化，以富集，细分和三角剖分的地下几何形状的算法开发，从360-GPR数据中进行。该项目的次要挑战是解决与传统GPR数据显示中特征识别相关的明显缺乏解释性清晰度，这使得非雷达训练的铁路行业操作员无法获得的结构信息无法获得。因此，Visualiser开发将引入一个基于深度学习的对象分类方案，以动态地突出显示和定性评估360-GPR数据中存在的地下功能（即资产，缺陷和媒介特征）实时。然后，通过设计和集成专用的简化用户界面，将提取的信息直观地传达给最终用户。此外，该项目旨在提高调查数据中可实现的手术浸入水平，从而促进虚拟检查的更符合人体工程学的执行，并允许对现场团队的结构特征进行不受限制的分析（例如，狭窄的culvert Interiors）。这将通过创建原始渲染引擎来实现，该引擎能够将从360-GPR数据插值的地下几何形状转换为现实纹理的3D虚拟环境。导航将利用商业VR硬件单元，将操作员直接放置在数字化重建的隧道中，而纹理现实主义将源于新算法的开发来融合360-GPR数据，并与相应的近乎冲突3D激光扫描相应的近乎近距离扫描。检查雷达系统跟踪。这项工作将涉及软件工程；使用VR硬件实用测试；机器学习，计算几何形状和人类计算机互动的主题；除了研究360-GPR数据倒置背后的物理和数学。