Intelligent Tunnel Surveillance Using Deep Learning

Abstract

Maintaining tunnel safety presents numerous challenges due to their restricted environments and specific conditions. This study proposes a cutting-edge system that leverages deep learning to improve tunnel surveillance efficiency and reliability. The framework incorporates advanced neural network models, particularly Region-based Convolutional Neural Networks (RCNN), to process real-time video feeds and detect risks like unauthorized entry, debris, and structural problems. Tailored with data specific to tunnel scenarios, the system achieves impressive accuracy, even under challenging circumstances such as dim lighting and reduced visibility. By streamlining anomaly detection and hazard prevention, this framework enhances safety protocols, minimizes reliance on manual oversight, and facilitates swift emergency responses. This innovative approach has the potential to transform tunnel management, ensuring greater safety and operational dependability.