I-Corps: Automated Pavement Evaluation Using Advanced Machine Learning

I-Corps：使用先进机器学习自动路面评估

• 批准号: 2229743
• 负责人: William Buttlar
• 金额: $ 5万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229743&HistoricalAwards=false
• 关键词: Corps; Automated; Pavement; Evaluation; Using

The broader impact/commercial potential of this I-Corps project is the development of a software suite that allows highway owner-agencies such as cities and state Departments of Transportation (DOTs) to manage their pavement networks in a more rigorous and cost-effective manner, enabling more strategic use of maintenance and rehabilitation funds. The software suite can be readily expanded to include other civil infrastructure, such as airfield pavements, bridges, and rail and transit track systems. In addition, other assets associated with or in the vicinity of transportation facilities can be located, identified, and assessed. This mapping includes road signs, guard rails, paint markings, utilities, garbage bins, etc., which can be viewed as additional layers on the data visualization platform of the software suite. Finally, measures of transportation sustainability, resilience, and environmental impact may be assessed and visualized as yet another layer on the visualization software. This visualization provides owner-agencies an unbiased, straightforward platform to assist them in systematically moving towards more sustainable and resilient infrastructure.This I-Corps project is based on the development of an automated pavement evaluation software suite. The suite will incorporate several coded and integrated machine learning and deep learning techniques used for the detection and classification of the extent and severity of critical pavement obstacles. The algorithms seek to provide highly accurate, unbiased pavement condition assessments. The machine learning-based models use a comprehensive pavement image dataset, which considers twenty different pavement distresses for both flexible and rigid pavements as verified by pavement experts. Another capability of the software is its ability to pre-screen projects using internet street view images. This capability may allow agencies to crowd-source data for their own road networks using municipal vehicle fleets equipped with the supplied video capture and road roughness sensor system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该I-Corps项目的更广泛的影响/商业潜力是开发软件套件，该套件允许高速公路所有者 - 城市和州运输部（DOTS）等公路所有者以更严格和更具成本效益的方式管理其人行道网络，从而更具战略性地利用维护和康复资金。该软件套件可以很容易地扩展到包括其他民用基础设施，例如机场路面，桥梁以及铁路和运输轨道系统。此外，可以找到，确定和评估与运输设施相关的其他资产。该映射包括路标，防护栏，油漆标记，公用事业，垃圾箱等，可以将其视为软件套件数据可视化平台上的其他层。最后，可以评估和可视化运输可持续性，弹性和环境影响的衡量标准作为可视化软件的另一层。这种可视化为所有者提供了一个公正，直接的平台，可帮助他们系统地朝着更具可持续性和弹性的基础架构迈进。该I-Corps项目基于开发自动的路面评估软件套件。 该套件将结合几种编码和集成的机器学习以及用于检测和分类关键路面障碍的程度和严重性的深度学习技术。 该算法试图提供高度准确，公正的路面条件评估。基于机器学习的模型使用综合的路面图像数据集，该数据集考虑了二十种不同的路面障碍，以实现路面专家验证的灵活和刚性路面。该软件的另一个功能是它可以使用Internet Street View Images进行预屏项目的能力。 这种能力可以允许代理商使用配备了提供视频捕获和道路粗糙度传感器系统系统的市政车舰队来为自己的道路网络提供众包数据。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来支持的。