This project is designed to identify accidents and categorize them as 'moderate' or 'severe' and instantly notify authorities through Email and WhatsApp messages, with the added visual cue of an RGB LED. The model is trained efficiently and the system is well optimized for it to work on a RaspberryPi.
Accidents on the road can have serious consequences, and quick response times are crucial for minimizing harm. This project aims to enhance road safety by automatically detecting accidents through object detection in real-time video streams (also in static images and videos) and sending a notification to concerned authorities. This repository contains the implementation of an accident detection system utilizing the YOLOv8 object detection model.
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
git clone https://github.com/gauravhegade/MCES-Accident-Detection.git Accident-Detection
cd
into the directory: cd Accident-Detection
, then proceed to Step 2detecting_static.py
available in ML Part
folderpython detecting_static.py
inputs
folder), and save the results in the results
directory.detecting_videostream.py
available in ML Part
folderpython detecting_videostream.py
stream_url
) by converting it to individual frames.