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

这个名为Smart Gauge的项目将使用点云数据，通过自动化改进铁路测量和计量技术。我们提出的系统将自动化工作，对连续和即时的真实的时间信息的铁路测量。目前的测量制度可能每5年进行一次，GMV计划将一个系统推向市场，每3到6个月减少一次。今后可能通过增加高精度（全球移动卫星专有）全球导航卫星系统技术和卫星图像等组成部分来扩大保真度，这将确保项目结束后继续保持效率。通过与伦敦交通局和铁路网的合作，我们将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的世界数据，以提高我们的模型性能在第一阶段。这种渐进式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中处于几何深度学习前沿的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。在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;在5米的点云切片中准确记录坡度、曲率和净空，并将这些数据写入符合国家计量数据库标准的SC 0。为了实现这些目标，我们的模型将执行对象识别和3D点云分割。总而言之，拟议的集成模型将消除行业目前面临的主要瓶颈（并被Network Rail强调为这场竞争的关键驱动力）;通过资产管理，他们将受益于更安全的客运和货运铁路;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，带来新的数据源并引领预测性维护。

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.