Document Type
Technical Report
Publication Date
4-15-2024
Keywords
UAV, unmanned aircraft systems, drones, traffic monitoring, incident management, YOLO 7 algorithm, vehicle detection
Digital Object Identifier (DOI)
https://doi.org/10.5038/CUTR-NICR-Y3-4-7
Abstract
The research initiative focuses on exploring the feasibility of using Unmanned Aerial Vehicles (UAVs) for incident detection in high-speed multi-lane and freeway corridors. Previous phases of this research have laid the groundwork for developing and validating UAV-based incident detection methodologies. The research methodology involved developing an incident detection methodology using real-time video data from single and multiple flying UAVs, UAV path planning for corridor incident detection, and designing experiments to establish protocols, standards, and guidance for using UAVs in accordance with FAA regulations. The study's conclusions highlights key insights into the performance of the YOLO7 algorithm, emphasizing the impact of factors such as drone elevation and type on its efficiency. The findings indicate that UAVs offer a promising alternative to traditional manual patrolling methods. The validation of object detection and incident detection algorithms further validates the feasibility of using UAVs for real-time traffic monitoring, emphasizing the importance of meticulous site selection and data collection processes.
Scholar Commons Citation
Cruzado, Ivette; Figueroa-Medina, Alberto M.; González, David R.; Valdés, Didier M.; Rivera-Pérez, Luz G.; Ruiz-Hernández, Bryan E.; and Dieppa-Ortiz, Josué A., "Freeway Incident Detection and Management using Unmanned Aircraft Systems (Phase III)" (2024). Research Reports. 41.
https://digitalcommons.usf.edu/cutr_nicr/41
Policy brief
