Traffic flow management by detecting and estimating vehicles density based on object detection model.

The huge growth in the number of vehicles is causing serious traffic management problems. Existing roads must handle traffic more than expected which presents serious challenges including congestion and safety. Intelligent traffic management systems were proposed as a solution to solve such problems. This intelligent system is charged to estimate vehicle density and manage the traffic flow accordingly. A wide variety of sensors was deployed for the traffic management system such as cameras, ultrasonic sensors, radar, infrared sensors, and acoustic sensors. In this work, an intelligent traffic flow management system was proposed based on data provided by public surveillance cameras. For this purpose, a real-time vehicle detection and counting model based on You Look Only Once (YOLO) v6 was deployed. The proposed model was trained and evaluated on different publicly available datasets such as BDD 100K and KITTI. Extensive experimentations proved the efficacy of the proposed model for detecting vehicles while operating under real-time constraints. The proposed traffic flow management system was evaluated using real videos for different traffic scenarios, and good results were achieved. [ABSTRACT FROM AUTHOR]