Project Description:
This AI-powered vehicle detection system leverages computer vision, deep learning, and AI to automatically detect and classify vehicles in real-time from surveillance footage. The project integrates YOLO-based object detection with Google Gemini AI to identify vehicle type, color, and manufacturer.
Using object tracking and image processing techniques, the system:
- Detects and tracks cars and trucks within a defined area.
- Crops and processes images of detected vehicles.
- Analyzes images using Gemini AI to extract detailed vehicle attributes.
- Logs extracted vehicle information with timestamps for future analysis.
This solution is designed for applications in traffic monitoring, law enforcement, automated toll collection, and smart surveillance.
Key Features:
✅ YOLO-Based Vehicle Detection – Detects cars and trucks in real-time.
✅ Object Tracking – Ensures vehicles are uniquely identified and processed only once.
✅ Automated Image Cropping – Extracts images of detected vehicles.
✅ Google Gemini AI Integration – Analyzes images to extract vehicle type, color, and manufacturer.
✅ Data Logging – Saves structured data for reporting and analysis.
✅ Multithreading – Ensures efficient image processing.
Technologies Used:
🔹 YOLO Object Detection (Ultralytics YOLO)
🔹 OpenCV for Image Processing
🔹 Google Gemini AI for Vehicle Analysis
🔹 Multithreading for Faster Processing
🔹 cvzone for On-Screen Display
Use Cases:
🚦 Traffic Management Systems – Helps authorities track and analyze vehicle movement.
👮 Law Enforcement – Can assist in identifying stolen or suspicious vehicles.
🏢 Smart Surveillance – Enhances security in parking lots, toll stations, and highways.
📊 Fleet Management – Automates vehicle tracking for logistics companies.
Conclusion:
This AI-powered vehicle detection and classification system combines the power of deep learning and computer vision to provide an automated, accurate, and real-time vehicle analysis solution. The integration with Google Gemini AI enhances the system’s intelligence, making it a valuable tool for smart surveillance and traffic management applications.

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