Crossroads Traffic Data Analysis Using Drone-Based Deep Learning

Abstract

This study proposes a method for monitoring traffic conditions via the collection of road traffic data using drones and deep learning-based analysis. Utilizing the YOLOv8 object detection model, we analyzed traffic videos captured at 45 and 90 degrees at intersections, which revealed differences in object detection capabilities based on the shooting angle. To address issues related to occluded areas during object tracking, we optimized the shooting angles, and achieved a final model with an average of 96.9% and 93.7% path and vehicle type recognition rate, respectively. This validates the effective utilization of drone-based traffic data collection and deep learning analysis in road traffic management. Further research is necessary for the development and application of more sophisticated traffic information systems in the future.