Vehiscan - Vehicle Detection and License Plate Recognition System

Lapis Project Final

[Vehiscan Demo]

Vehiscan is an advanced vehicle detection and license plate recognition system that utilizes computer vision and deep learning to monitor traffic, count vehicles, and identify license plates in real-time or from uploaded videos.

Features

• 🚗 Vehicle Detection: Identifies cars, trucks, buses, and vans using YOLO object detection
• 🔢 Vehicle Counting: Tracks vehicles across counting lines/zones with direction detection
• 🚘 License Plate Recognition: Extracts and reads license plates using OCR (Optical Character Recognition)
• 📊 Statistics Collection: Maintains counts by vehicle type and plates detected
• ☁️ Cloud Integration: Uploads processed videos and data to AWS S3
• 🎥 Dual Processing Modes:
  • Real-time video stream processing
  • Uploaded video file processing

Technologies Used

• Computer Vision: OpenCV
• Deep Learning:
  • YOLO (You Only Look Once) for object detection
  • SORT (Simple Online and Realtime Tracking) for vehicle tracking
• OCR: EasyOCR for license plate reading
• GUI: Flet (Python framework for building interactive UIs)
• Cloud: AWS S3 for data storage
• Video Processing: FFmpeg

🔧 Installation & Setup

1. Clone this repository (the main project):

   git clone (https://github.com/haelahpx/VehiScan.git)
   cd VehiScan
   

2. Clone the SORT tracking repository (used for tracking vehicles):

   gh repo clone abewley/sort

3. Download or make sure you have the requirements.txt file for this project.

4. Install the required dependencies:

   pip install -r requirements.txt

5. Run the application:

   python main.py

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