🚗💥 Real-Time Accident Detection with YOLOv5 & Streamlit A real-time accident detection system using YOLOv5, OpenCV, and Streamlit, enhanced with sound alerts, logging, and live dashboard updates. Designed to detect collisions between vehicles in a video stream and log incidents with frame evidence and timestamps.

🌟 Features 🔍 YOLOv5 Object Detection Detects vehicles (cars, trucks, buses, bikes) in real-time using YOLOv5s.

🖼️ Live Video Feed with Streamlit Displays a real-time webcam stream with frame counter and collision alerts.

💥 Collision Detection Logic Uses bounding box overlap (IoU) to detect accidents based on object proximity.

📁 Frame Capture & Evidence Logging Saves frames of detected accidents in an evidence/ folder with a CSV log.

🔊 Real-Time Sound Alerts Plays an alert sound (via pygame) when a crash is detected.

🧠 Tech Stack Tool Purpose YOLOv5 Real-time vehicle detection OpenCV Video stream handling Streamlit Live UI dashboard Pandas Logging accident data to CSV Pygame Playing alert sounds Python Overall integration and logic 📦 Installation

Clone the repository

git clone https://github.com/your-username/accident-detection-yolov5 cd accident-detection-yolov5

Install dependencies

pip install -r requirements.txt

Or install manually:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu pip install opencv-python streamlit pandas pygame Make sure you have a working webcam and Python 3.8+.

▶️ Run the App streamlit run app.py Once launched:

A live camera feed will start.

When an accident is detected, you'll:

Hear a sound

See an alert in the UI

Get a frame saved in /evidence

See logs in logs.csv

📁 Project Structure ├── app.py # Main Streamlit App ├── alert.mp3 # Sound file for alert ├── logs.csv # Logged accident info ├── evidence/ # Captured frames └── requirements.txt # Dependencies 🔮 Future Improvements 🚦 Accident type classification (rear-end, side collision, etc.)

🧠 Integrate with vehicle tracking and motion estimation

🌐 Dashboard with map integration or multiple cameras

🧩 Deployable version for Raspberry Pi or Jetson Nano

📸 Preview

A live dashboard view with real-time accident detection and alerting.

📜 License This project is licensed under the MIT License.

🤝 Contributing Got ideas or want to improve the logic/model? Pull requests and suggestions are always welcome!