🔹 Description

The Car Crash Detection system is an intelligent computer vision application designed to automatically identify vehicle accidents using video input. The system analyzes visual data in real time to detect crash events and generate alerts, reducing reliance on manual reporting and enabling quicker emergency response.

Using the YOLOv8 object detection model, the system identifies vehicles and monitors their movement patterns. Sudden changes in speed, collisions, or abnormal vehicle behavior are detected through visual analysis powered by OpenCV and deep learning models trained using PyTorch.

Once an accident is detected, the system triggers an alert mechanism to notify authorities or emergency services, helping minimize response time and potentially saving lives. The solution is designed to be scalable and can be integrated with CCTV systems, traffic cameras, or smart city infrastructure.

This project demonstrates advanced knowledge of computer vision, deep learning, real-time video processing, and AI-based safety systems.

🔹 Key Features

• Real-time vehicle and accident detection
• Deep learning-based object detection using YOLOv8
• Visual analysis of collision and abnormal motion
• Automated alert triggering for authorities
• High-accuracy detection with trained models
• Scalable for CCTV and traffic monitoring systems

🔹 Technologies Used

• Programming Language: Python
• Computer Vision: OpenCV
• Deep Learning Framework: PyTorch
• Object Detection Model: YOLOv8