AUTOMATED SURVEY PROCESSING FOR RAILWAY STRUCTURE GAUGING

铁路结构测量的自动化测量处理

• 批准号: 971731
• 金额: $ 12.71万
• 依托单位: ATKINS 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=971731
• 关键词: AUTOMATED; SURVEY; PROCESSING; RAILWAY; STRUCTURE

Currently the track side infrastructure such as platform, viaduct, tunnel etc. are scanned using laser measurement systems for Network Rail and categorized into structure types manually before storing in a National Gauging Database (NGD). The NGD stores information about the railway infrastructure and is used to calculate clearances between trains and infrastructure. Currently processing of survey data is a major bottle neck in structure gauging, with it taking years rather than months before data collected reaches the database in a useable format for gauging calculations. As a result much of the data is out of data in needs further human input to manage clearances and determine actions required to manage clearances for new train introductions. In some cases, this includes manual resurveying of platforms and other structures to obtain current information. This Atkins research and development project aims to improve data processing and availability by applying machine learning techniques using our expertise in developing Artificial Intelligence (Specifically Convolutional Neural Networks) processes to automatically classify the scanned data, as well as identifying any vegetation. The infrastructure data will be considered in a 3D Corridor Projection Space instead of 2D sections to provide better performance. This method will provide more greater confidence in the data leading to more accurate understanding of the clearances between structure and track. This will be achieved by the efficiency increases to process the data once the method is fully developed. This will lead to an NGD with more up to date, consistent and accurate survey data. A result there will be a reduction of survey data processing time with overall reduced costs and timescales for the introduction of new trains or cascading trains to new lines. SNC-Lavalin’s acquisition of the Atkins group of companies created a global fully integrated professional services and project management company. Together, we have over 50,000 employees. Through integrating our workforce we deliver a compelling offer to our clients in the Railway and Transportation sector. This project will combine the skills of SNC-Lavalin’s Gauging and Infrastructure teams and Atkins Software and Data Science teams, bringing together a technical team with all the key area.

目前，轨道侧基础设施，如站台、高架桥、隧道等，都是使用网络铁路激光测量系统进行扫描，并在存储到国家轨距数据库(NGD)之前手动分类。NGD存储有关铁路基础设施的信息，并用于计算列车和基础设施之间的间隙。目前，测量数据的处理是结构测量中的一个主要瓶颈，在收集的数据以可使用的格式进入数据库进行测量计算之前，需要数年而不是数月的时间。因此，许多数据都不在数据中，需要进一步的人工输入来管理许可并确定管理新列车引入的许可所需的行动。在某些情况下，这包括人工重新审查平台和其他结构，以获取最新信息。这个Atkins研发项目旨在利用我们在开发人工智能(特别是卷积神经网络)过程中的专业知识，通过应用机器学习技术来改进数据处理和可用性，以自动对扫描的数据进行分类，以及识别任何植被。基础设施数据将考虑在三维走廊投影空间中，而不是二维横断面中，以提供更好的性能。这种方法将对数据提供更大的置信度，从而更准确地了解结构和轨道之间的间隙。一旦该方法完全发展，将通过提高处理数据的效率来实现这一点。这将导致NGD拥有更多最新、一致和准确的调查数据。因此，将缩短调查数据处理时间，降低在新线路上采用新列车或级联列车的总成本和时间表。SNC-Lavalin收购了阿特金斯集团公司，创建了一家全球全面整合的专业服务和项目管理公司。我们总共有50,000多名员工。通过整合我们的员工队伍，我们为铁路和运输领域的客户提供了极具吸引力的服务。该项目将结合SNC-Lavalin的测量和基础设施团队以及Atkins软件和数据科学团队的技能，将一个拥有所有关键领域的技术团队聚集在一起。