Dashcam-Enabled Deep Learning Applications for Airport Runway Pavement Distress Detection

23-8193Pavement distress detection plays a vital role in ensuring the safety and longevity of runway infrastructure. This project presents a comprehensive approach to automate distress detection and geolocation on runway pavement using state-of-the-art deep learning techniques. A Faster R-CNN model is trained to accurately identify and classify various distress types, including longitudinal and transverse cracking, weathering, rutting, and depression. The developed model is deployed on a dataset of high-resolution dashcam images captured along the runway, allowing for real-time detection of distresses. Geolocation techniques are employed to accurately map the distresses onto the runway pavement in real-world coordinates. The system implementation and deployment are discussed, emphasizing the importance of a seamless integration into existing infrastructure. The developed distress detection system offers significant benefits to the Utah Department of Transportation (UDOT) by enabling proactive maintenance planning, optimizing resource allocation, and enhancing runway management capabilities. Future potential for advanced distress analysis, integration with other data sources, and continuous model improvement are also explored. The project showcases the potential of low-cost dashcam solutions combined with deep learning for efficient and cost-effective runway distress detection and management

Doctor of Philosophy

dissertationUnmanned Aircraft Systems (UAS) promise to be a disruptive technology, providing businesses and the general public with advanced robotic tools at low costs. However, the anticipated scale and density of operations make complete human control impractical, and it is necessary to develop the cyber-physical science, technology and engineering methods to design, develop and safely control these tightly coupled cyber-physical systems. The research presented here is motivated by a specific problem in UAS Traffic Management (UTM) known as Strategic Deconfliction, a conflict management strategy that is achieved through airspace organization and management, demand and capacity balancing, and traffic synchronization. This research introduces a lane-based approach to strategic conflict management. Methods for constructing aerial corridors and scheduling flights within them are given, and issues that arise in UTM are explored under this framework. It is shown that the computational complexity of strategic deconfliction under the lane-based approach is reduced for individual operators, and additionally enables operators to assess the state of the airspace more effectively for contingency handling. To assess the performance characteristics of lane-based systems, and to compare this strategy to what is currently proposed by the FAA and NASA, a set of measures including flow, density, and other network properties that have been traditionally used for ground-based traffic are applied to UTM. The trajectory of this research was informed by an effort to commercialize it, and it is currently being funded by two grants from the Utah Department of Transportation, as well as a Small Business Technology Transfer (STTR) program in collaboration with the United States Air Force. Additionally, the author of this dissertation participated in NASA's Advanced Air Mobility (AAM) National Campaign Developmental Test (NC-DT) X3 simulations. The X3 simulations were the first time that multiple industry partners were gathered by NASA to test and collaborate on software implementations for AAM, with particular emphasis on strategic conflict management