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Computer vision-based condition assessment of the public transit infrastructure assets

Computer vision-based condition assessment of the public transit infrastructure assets
基于计算机视觉的公共交通基础设施资产状况评估
批准号:
561003-2020
负责人:
RezazadehAzar, Ehsan
金额:
$2.19万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2020
资助国家:
加拿大
项目状态:
已结题
起止时间:
2020-01-01 至 2021-12-31

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中文摘要
翻译
公共交通系统对加拿大的社会和经济繁荣至关重要,并有助于促进社会公平和环境目标。高效和有效的基础设施资产管理对于提供可靠的公共交通系统至关重要,但目前的基础设施资产管理实践主要依赖于传统的数据收集方法,这些方法需要人工进行现场访问,这是一项劳动密集型和耗时的工作。由于公共收入不稳定且有限,有效的资产管理对中小城市来说可能更具挑战性。由于交通机构在利用有限的人力资源维护和更新大型数据集方面存在困难,许多研究工作都集中在半自动化基础设施状况评估方法的开发上,例如在线社区的众包、图像和视频处理、航空摄影和基于车辆的激光雷达。拟议的研究项目将开发一个创新的基于计算机视觉的系统,以改善公共交通基础设施资产管理的数据收集和状况评估过程。拟议的系统将利用公共汽车的常规运行进行数据收集,然后利用深度学习方法检测公共交通基础设施资产,评估其状况,并更新资产管理清单。这个研究项目的行业合作伙伴是雷湾运输公司和康赛特Telematics公司。桑德贝运输公司在桑德贝地区提供一般公共交通服务,旨在将本研究的结果纳入其基础设施资产的管理。Consat Telematics是一家开发智能交通系统的技术公司,开发的系统将集成到他们的车队管理系统中。
英文摘要
Public transit systems are essential to the social and economical prosperity of Canada, and contribute to the advancement of social equity and environmental objectives. Efficient and effective infrastructure asset management is vital to providing a reliable public transit system, but current infrastructure asset management practices mainly rely on conventional data gathering methods which require manual site visits that are labour-intensive and time-consuming. Effective asset management could be more challenging for small and medium-sized cities due to unsteady and limited public revenues. Since transit agencies have difficulties in maintaining and updating their large datasets using their limited human resources, many research efforts have focused on development of semi-automated infrastructure condition assessment methods, such as crowdsourcing by the online community, image and video processing, aerial photography, and vehicle-based LiDAR. The proposed research project will develop an innovative computer vision-based system to improve data collection and condition assessment processes for public transit infrastructure asset management. The proposed system will use the regular operation of the transit buses for data collection and then utilizes deep learning methods to detect public transit infrastructure assets, asses their condition, and update asset management inventories. The industry partners in this research project are Thunder Bay Transit and Consat Telematics. Thunder Bay Transit provides general public transit services in Thunder Bay area and aims to incorporate the findings of this research to enhance management of their infrastructure assets. Consat Telematics is a technology company which develops intelligent transportation systems and the developed system will be integrated into their fleet management systems.
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Automated data collection and machine learning methods for civil infrastructure condition assessment in sparsely inhabited regions of Canada
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  • 项目类别:
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  • 资助金额:
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  • 财政年份:
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Automated data collection and machine learning methods for civil infrastructure condition assessment in sparsely inhabited regions of Canada
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  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.89万
  • 财政年份:
    2021
  • 负责人:
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Smart vision-based monitoring system for heavy construction and surface mining jobsites
  • 批准号:
    RGPIN-2015-03812
  • 项目类别:
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  • 资助金额:
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  • 财政年份:
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  • 批准号:
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  • 项目类别:
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  • 资助金额:
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  • 批准年份:
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  • 负责人:
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