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

Project Code :TCMAPY1060

Objective

The primary objective of this project is to pioneer an advanced accident classification system tailored specifically for tunnel environments by employing deep learning algorithms. Recognizing the shortcomings of current detection systems, our goal is to design, implement, and test a model that can accurately identify and categorize incidents in real-time, regardless of the inherent visibility and spatial challenges posed by tunnels. Through the analysis of a comprehensive dataset of tunnel accidents, we aim to optimize model performance and ensure its adaptability to diverse scenarios. Ultimately, the project seeks to enhance safety standards, reduce response times, and bolster confidence in tunnel transportation systems.

Abstract

This research presents an innovative approach to accident classification within tunnels using deep learning algorithms. Given the unique challenges posed by tunnel environments, such as limited visibility and confined spaces, effective accident detection is paramount for ensuring swift response and safety. Utilizing a dataset comprising various tunnel accidents, we trained and evaluated multiple deep learning models. Our results show a significant improvement in classification accuracy compared to traditional methods. The proposed system demonstrates potential for real-time monitoring and alerting in tunnel infrastructure, emphasizing the utility of deep learning in enhancing transportation safety in constrained environments.

Keywords: Accident in tunnel classification dataset and deep learning algorithms

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.