AI-Based Real-Time Vehicle Crash Detection and Automated Emergency Alert System Using Deep Learning

Abstract

Road accidents are one of the leading causes of fatalities worldwide, especially in developing countries where timely medical assistance is often delayed. Immediate detection of vehicle crashes and rapid communication with emergency services can significantly reduce mortality rates. This project proposes an AI-based real-time vehicle crash detection and automated alert system that leverages deep learning and computer vision techniques to identify accidents from live video streams or recorded footage. The system utilizes a pre-trained deep learning model built using Tensor Flow Object Detection API to analyze video frames and detect crash-related patterns. Video input can be sourced from CCTV cameras, dash cams, or stored video files. Each frame is processed and passed through the trained model, which identifies objects and predicts whether a crash event has occurred based on confidence thresholds. The system continuously monitors detection scores and triggers an alert only when consistent crash detection is observed over multiple frames, ensuring reliability and reducing false positives. Once a crash is detected, the system captures relevant frames, enhances image quality, and stores them for further analysis. Simultaneously, it activates an automated alert mechanism that sends notifications via email and SMS to nearby emergency services such as hospitals, police stations, and regional transport offices (RTOs). This dual-alert system ensures redundancy and increases the likelihood of a timely response. A user-friendly graphical interface is developed using Tkinter, allowing users to load video sources, monitor detection results in real time, and visualize system outputs. The interface also displays detection progress and system status updates, improving usability for non-technical users. The proposed system integrates multiple technologies including Open CV for video processing, Tensor Flow for deep learning inference, and threading for concurrent execution of detection and alert modules. The architecture ensures efficient processing and minimal latency, making it suitable for real-time deployment. Experimental results demonstrate that the system can effectively detect vehicle crashes with high accuracy under varying lighting and environmental conditions. The use of a high confidence threshold and multi-frame validation reduces false alarms significantly.In conclusion, this system provides an intelligent, automated solution to enhance road safety by enabling rapid accident detection and emergency response. It has the potential to be deployed in smart cities, highways, and traffic surveillance systems to save lives and improve emergency management.