Development of Drone-Assisted Highway Mowing Operations Planning, Monitoring, and Verification Capabilities

This project focused on the development for the integration of unmanned aerial systems (UASs) and artificial intelligence (AI) to automate planning, monitoring, and performance verification of highway maintenance tasks within the Georgia Department of Transportation (GDOT). The current mowing assessment for verifying the performance of mowing contractors was conducted by labor-intensive and qualitative visual inspection. Recent technology in AI and drones brings an opportunity to automate and assess the verification of mowing performance to improve efficiency and speed up the inspection process. In this report, the research team focuses on three main tasks: (1) optimizing the workflow analysis for the GDOT mowing assessment, (2) developing a data-driven automated highway monitoring system, and (3) integrating a user-friendly interface for the GDOT Office of Maintenance. In the workflow analysis, the research team identifies the optimal integration of AI and UAS usages and provides a workflow diagram to recommend the best use of the proposed system. In the data-driven highway monitoring system, the research team develops a machine learning-based grass mowing assessment framework using image data collected from drones. The user interface incorporates the proposed system and provides clear and useful information for GDOT. The contribution of the proposed solutions could support the GDOT tasks of verifying the mowing performance of contractors and expand the application of AI and drone technology within GDOT. The full text of the report is available online at https://g92018.eos-intl.net/eLibSQL14_G92018_Documents/20-09.pdf