Smart Gauge - Automatic rail survey processing and gauging using deep learning.

这个名为智能轨距的项目将使用点云数据通过自动化来改进铁路测量和测量技术。我们建议的系统将自动化工作，以获得关于钢轨测量的连续和即时实时信息。目前的测量制度可能最多每5年进行一次，GMV计划将一种系统推向市场，该系统将减少每3至6个月一次的间隔时间。未来可能通过增加高精度(GMV专有)全球导航卫星系统技术和卫星图像等组件来扩展保真度，以确保项目结束后持续的效率。我们将通过与伦敦交通局和Network Rail的合作，更好地了解用于测量的最新市场技术和实践。我们将共同努力，包括专业知识评估和提供真实世界数据，以提高我们的模型在第一阶段的性能。这种渐进的对话将导致强大的最终用户参与，这将促进更快的商业化和反馈，成为市场上自动测量处理系统的领导者。我们将通过利用过去几年几何深度学习的前沿技术来应对技术挑战，特别是通过自动驾驶汽车行业的投资。第一阶段要解决的两个主要目标是：对10种类型的结构和植被进行分类；准确地记录5米切片点云的铁路超高、曲率和间隙，并将这些数据写入符合国家测量数据库标准的SC0。为了实现这些目标，我们的模型将对三维点云进行对象识别和分割。总而言之，拟议的综合模式将消除行业目前面临的主要瓶颈(Network Rail强调为这一竞争的关键驱动力)；通过资产管理，使它们受益于对乘客和货运更安全的铁路；在繁琐的体力劳动支出方面的财务效率；以及访问直观的界面，该界面带来新的数据源，并在预测性维护方面处于领先地位。

This project, Smart Gauge, will use point cloud data to improve railway surveying and gauging techniques through automation. Our proposed system will automate work towards continuous and instant real time information on rail gauging. The current surveying regime may take place up to every 5 years, GMV plan to bring to market a system which will reduce this internval every 3 to 6 months. Possible future extensions of fidelity through the addition of components such as high precision (GMV proprietary) GNSS technology, and satellite imagery, will assure continuous efficiency after the project ends. We will gain a better perception of up-to-date market technologies and practices used for gauging through our partnership with TfL and Network Rail. We will work together to include expertise assessment and provision of real world data to boost our model performance during Phase 1. This progressive conversation will lead to a strong end-user engagement that will facilitate faster commercialisation and feedback to be the leaders in automatic gauging processing systems in the market. We will meet the technical challenges by utilising technologies at the forefront of Geometric Deep Learning over the last few years, particularly through investment from the self-driving car industry. The two main objectives to be addressed during Phase 1 are: to categorise 10 types of structure and vegetation; and to accurately record cant, curvature and clearance in 5 metre slices of point cloud and write these data to SC0 compliant with the National Gauging Database standard. To fulfil these objectives, our model will perform object identification and segmentation of 3D point clouds. To summarise, the proposed integrated model will remove the major bottleneck currently faced in industry (and highlighted as a key driver for this competition by Network Rail); so that they benefit from a safer railway for passengers and freight, through asset management; financial efficiency in terms of tedious manual labour expenditure; and access to an intuitive interface that brings new data sources and leads the way in predictive maintenance.