Accident Detection on Roads and Tunnels Using Deep Learning Technique

This examination presents an inventive way to deal with mishap characterization inside burrows utilizing profound learning calculations. Given the extraordinary difficulties presented by burrow conditions, for example, restricted perceivability and bound spaces, successful mishap location is principal for guaranteeing quick reaction and security. Using a dataset containing different passage mishaps, we prepared and assessed various profound learning models. Our outcomes show a huge improvement in order precision contrasted with customary techniques. The proposed framework shows potential for ongoing checking and cautioning in burrow foundation, underscoring the utility of profound learning in improving transportation security in obliged conditions.