Enhancing University Safety through AI-Powered Speed Detection

Ensuring the safety of individuals within university campuses is a critical concern for academic institutions worldwide. In this study, we propose a novel approach leveraging Artificial Intelligence (AI) and computer vision technologies to enhance safety measures, particularly focusing on speed detection and traffic management within university campuses. By leveraging the capabilities of YOLOv9c with high-resolution surveillance systems, our proposed platform aims to detect and regulate vehicle speeds effectively, thus mitigating potential risks and enhancing overall safety for students, faculty, and staff members. Our system can detect and track vehicles in real-time, enabling precise speed estimation without the need for specialised hardware or manual intervention. We evaluate the performance of our approach on a diverse dataset of traffic videos and demonstrate its effectiveness in accurately detecting vehicle speeds under various conditions. Our results indicate that the proposed system outperforms existing methods in terms of accuracy, speed, and scalability, making it a promising solution for real-world speed detection applications.