Smart traffic monitoring with YOLOv9 object detection algorithm

Abstract

Rapid advancements in artificial intelligence technology have enabled computer vision to be utilized across a wide range of engineering disciplines. This study examines the practical solutions offered by image processing technology in manual counting applications and the accuracy of advanced algorithms. The applicability and performance of the YOLOv9 algorithm in traffic counts have been evaluated. The research shows that the algorithm operates with high accuracy and minimizes human error. The study involves classification and counting operations for three different types of vehicles. According to the results, cars and trucks are detected with over 95% accuracy, while smaller objects like motorcycles have slightly lower accuracy. The effective use of YOLOv9 in vehicle counting and traffic management applications highlights the importance of object detection technology in intelligent transportation systems. This study demonstrates the potential of this technology to improve efficiency in traffic management, providing guidance for future applications. The key role that advanced algorithms like YOLOv9 can play in the development of intelligent transportation systems is an important topic for future researchers and industry professionals.