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

智能测量 - 使用深度学习自动进行铁路测量处理和测量。

• 批准号: 971730
• 金额: $ 13.55万
• 依托单位: GMV INNOVATING SOLUTIONS LIMITED
• 依托单位国家: 英国
• 项目类别: Small Business Research Initiative
• 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=971730
• 关键词: Smart; Gauge; Automatic; rail; survey

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.

这个名为“智能测量”的项目将利用点云数据通过自动化来改进铁路测量和测量技术。我们提出的系统将自动化工作，以实现铁路测量的连续和即时实时信息。目前的调查制度可能每5年进行一次，GMV计划将一个系统推向市场，每3至6个月将减少这一间隔。未来可能通过增加高精度（GMV专有）GNSS技术和卫星图像等组件来扩展保真度，将确保项目结束后的持续效率。通过与伦敦交通局和铁路网的合作，我们将更好地了解最新的市场技术和测量方法。我们将共同努力，包括专业评估和提供真实世界的数据，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终端用户参与，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。我们将利用过去几年几何深度学习的前沿技术，特别是通过自动驾驶汽车行业的投资，来应对技术挑战。第一阶段的两个主要目标是：将10种结构和植被分类；准确记录点云5米切片的斜度、曲率和间隙，并写入符合国家测量数据库标准的SC0。为了实现这些目标，我们的模型将执行三维点云的目标识别和分割。总而言之，拟议的综合模式将消除目前工业面临的主要瓶颈（并强调作为网络铁路竞争的关键驱动因素）；通过资产管理，他们可以从更安全的客运和货运铁路中获益；从繁琐的手工劳动支出来看，财务效率；访问直观的界面，带来新的数据源，并引领预测性维护的方式。