SBIR Phase I: Automating Element-Level Inspection of Civil Infrastructures through Computer Vision

这个小企业创新研究（SBIR）第一阶段项目的广泛影响是开发一个损伤检测分析软件平台，以降低重型民用基础设施资产（例如桥梁和水坝）的故障风险，同时使用人工智能（AI）和土木工程的独特组合优化这些重要系统的资本配置。负责民用基础设施维护和评估的公用事业公司需要快速、可靠的解决方案来收集大量数据，并快速评估大量重型民用基础设施的状态。他们还需要能够准确及时地向利益相关者报告这些资产的结构完整性。该项目旨在促进民间领导人在基础设施投资方面的数据驱动决策。这项小型企业创新研究（SBIR）第一阶段项目将开发和商业化技术，以提高基础设施资产管理过程的自动化程度，并降低资产所有者在这一过程中的成本。自动化程度的提高也可能通过人工智能分析改善条件和时间变化评估。由此产生的分析将通过提高准确性和客观性来改进检查实践，从而导致更安全的基础设施系统和更少的基础设施故障。该一期项目的技术创新是一个3D计算机视觉管道，旨在处理遥感数据并将其自动转换为满足工程师报告需求的格式。该技术通过开发基础技术能力，将3D点云自动分割并转换为基础设施组件的高分辨率2D正形图，解决了与使用3D遥感数据进行基础设施资产管理相关的长期挑战。现有的方法既不灵活也不一般化，要么采用高度约束的几何方法，要么采用统计深度学习模型，而相关的基础设施数据集并不存在。所提出的方法将利用计算几何分析将点云分离并分割成单个结构组件。该流程将通过用户试验研究和正在进行的客户发现和验证活动进行原型和修订。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to develop a damage detection analysis software platform to reduce the risk of failure for heavy civil infrastructure assets (e.g., bridges and dams) while optimizing the capital allocation for these vital systems using a unique combination of artificial intelligence (AI) and civil engineering. The public utilities responsible for civil infrastructure upkeep and assessment need fast, reliable solutions to collect massive amounts of data and rapidly assess the status of large amounts of heavy civil infrastructure. They also need to be able to make accurate and timely reports to stakeholders on the structural integrity of these assets. This project aims to facilitate data-driven decision making for civic leaders regarding infrastructure investments. This Small Business Innovation Research (SBIR) Phase I project will develop and commercialize technologies to increase the automation of the infrastructure asset management process and reduce the costs of this process for asset owners. This increase in automation may also lead to improved conditions and temporal change assessment through AI-powered analytics. The resulting analytics would improve inspection practices by increasing accuracy and objectivity, leading to safer infrastructure systems and fewer infrastructure failures. The technical innovation of this Phase I project is a 3D computer vision pipeline designed to process remotely sensed data and automatically transform it into a format that meets the reporting needs of engineers. The technology addresses long-standing challenges associated with using 3D remote sensing data for infrastructure asset management by developing the foundational technical capabilities to automatically segment and transform 3D point clouds into high-resolution 2D orthomosaics of infrastructure components. Existing approaches are not flexible or generalizable, either employing highly constrained geometric approaches or statistical deep learning models, for which relevant infrastructure data sets do not exist. The proposed approach will utilize computational geometric analysis to isolate and segment point clouds into individual structural components. The process will be prototyped and revised through user pilot studies and ongoing customer discovery and validation activities.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.