Integrating Vision- Based Statistical Measures for Traffic Sign Recognition and Accident Mitigation Using YOLOv5

Rapid urbanization and increased vehicular density have led to the rise of complex traffic environments and a growing rate of accidents in the present time. This paper explained about the integration of advanced technologies that are deep learning and computer vision into transportation systems for road safety improvement. In a paper that focuses on real-time traffic sign recognition and accident prevention we show that the use of traffic signs is of vital importance to guide drivers with problems occurring due to human errors and environmental factors. Here we analyze the implementation of the state-of-the-art object detection model YOLOv5 because of its real-time image processing capabilities, which enable precise identification of nearby objects and traffic signs. It deals with the methodology of how to compile and annotate varied datasets, train the YOLOv5 model, and integrate it into a real-time processing pipeline. Furthermore, it tests the system's performance across various traffic scenarios focusing on robustness against occlusions and variability in the environment. Results indicate that AI -driven solutions can greatly enhance situational awareness and reduce accident rates. This survey integrates existing research not only on traffic sign recognition and object detection but also identifies gaps and future directions of research to contribute towards safer transportation systems.