A Real-Time Traffic Monitoring System Based on YOLOv8 for Vehicle Detection and Classification - Castillo, Jimwell; Rosculete, Robin; Babayans, Raymond Gharapeti; Merzoian, Anthony; Groves, Kaylee; Calva, Gisela; Dollente, Alma; Jia, Xudong; Li, Bingbing; Jiang, Xunfei (IEEE Green Energy and Systems Conference, 2024)
Urban traffic congestion presents significant challenges that require efficient and intelligent management solutions. Traditional traffic monitoring methods lack precision and adapt-ability, especially under varying conditions. Our project aims to develop a real-time traffic monitoring system using 2D camera technology and machine learning algorithms to address these gaps. The system includes a camera array for data collection, a data processing unit, and a user interface for real-time monitoring. Our solution employs YOLOv8 for accurate vehicle detection and classification and is demonstrated through rigorous testing. The system offers a robust framework for enhancing urban traffic management, reducing congestion, and improving road safety. Experimental results highlight the system's effectiveness in data aggregation, storage, and real-time processing.