Car Crowd Management using Proposed YOLOv5 Model
Car crowd management refers to the process of efficiently and safely managing the movement and flow of cars in crowded areas, such as parking lots, traffic intersections, event venues, and busy streets. Effective car crowd management is essential to ensure smooth traffic flow, prevent accidents, reduce congestion, and optimize the utilization of available parking spaces. It is a critical aspect of urban planning and traffic management to enhance the overall transportation experience and safety for both drivers and pedestrians. In this paper, a system was proposed for managing car crowds at traffic intersections based on deep learning algorithms, where a model of the Yolo v5 algorithm was proposed for the purpose of increasing its ability to detect cars at traffic intersections and through which the number of cars in each direction is determined. Depending on the number of cars in each direction, the priority of traffic in traffic lights is determined. Three databases were used for the purpose of examining the proposed system in identifying cars, and the accuracy of the proposed model reached 99% Which is an excellent percentage compared to previous works.