Intelligent Tunnel Surveillance Using Deep Learning

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.