Comparative Performance of YOLOv9 and YOLOv10 for Vehicle Detection Towards Real-Time Traffic Surveillance with UAVs

Intelligent transportation systems (ITS) have gained significant traction since the 1980s and 1990s, driven by technological advancements and increasing urbanization, which have caused intense transportation challenges. Drone systems, with their superior imaging capabilities, offer critical solutions for swift traffic surveillance, surpassing traditional monitoring systems. In this context, object recognition is crucial, and the YOLO algorithm stands out for its speed and efficiency. This study conducts a detailed performance evaluation of the YOLOv9 and YOLOv10 networks for motor vehicle classification through aerial images captured by drone platforms. Datasets were created from these UAV-based traffic images, and the performances of both algorithms were measured and compared. The results were analyzed to highlight YOLOv9 and YOLOv10's strengths and drawbacks. Additionally, the study discusses qualitative aspects, including advantages, disadvantages, and potential improvements for both algorithms in aerial traffic monitoring.