An Application of a Deep Learning Algorithm for Automatic Detection of Unexpected Accidents Under Bad CCTV Monitoring Conditions in Tunnels

This project introduces an innovative application of a deep learning algorithm for the automatic detection of unexpected accidents in tunnels, particularly under challenging CCTV monitoring conditions. Leveraging advanced deep learning techniques, the system enhances accident detection accuracy in adverse scenarios, such as low light conditions, smoke, and obscured visibility. The deep learning model is trained on diverse datasets to recognize anomalies indicative of accidents, ensuring robust performance in varying environmental conditions. The proposed application addresses the critical need for swift and accurate accident detection in tunnel environments, contributing to enhanced safety measures and efficient incident response. By overcoming challenges posed by adverse monitoring conditions, the project aligns with the broader goal of improving tunnel safety and mitigating the impact of unforeseen incidents.

Keywords:

Deep Learning Algorithm, Accident Detection, CCTV Monitoring, Tunnel Safety, Anomaly Recognition, Environmental Conditions, Incident Response, Safety Measures, Unexpected Accidents, Adverse Monitoring Conditions.