🚦 Automated Traffic Light Plate Detection & Boom Barrier System

Overview

This project is a real-time, AI-powered vehicle license plate detection and violation logging system designed for smart traffic management at intersections, tolls, or restricted areas. It integrates computer vision, OCR, and Arduino-based hardware to detect vehicles, recognize license plates, log violations, and control a physical boom barrier—all with robust fallback mechanisms and API/email integration.

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Features

• Real-time camera feed with live overlays and status indicators
• YOLOv11 deep learning for accurate license plate detection
• Haar Cascade fallback for detection robustness
• EasyOCR & Tesseract for high-accuracy license plate text extraction
• Ultrasonic sensor via Arduino for vehicle presence detection
• Automated boom barrier control (via Arduino & servo)
• Violation logging with cooldown to prevent duplicates
• Automatic violation image saving for evidence
• Email notifications with payment links (Cashfree integration)
• API integration for backend logging and analytics
• Keyboard simulation mode for testing without hardware
• Movable detection line (drag with mouse)
• Multi-threaded for smooth, real-time performance

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System Architecture

1. Camera captures live video.
2. Arduino (with ultrasonic sensor) detects vehicle presence and controls the boom barrier.
3. YOLO/Haar detects license plates in the video frames.
4. EasyOCR/Tesseract extracts text from detected plates.
5. Violation logic checks if a vehicle is present and if a new violation should be logged.
6. API/Email sends notifications and logs events.
7. Boom barrier is opened/closed automatically for detected vehicles.

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How It Works

1. Vehicle Detection:
   Arduino with an ultrasonic sensor detects when a vehicle is present (distance < 5cm) and signals the Python system.
2. Frame Processing:
   When a vehicle is detected, the camera frame is processed for license plate detection using YOLO. If YOLO fails, Haar cascade is used as a fallback.
3. Plate Cropping & Enhancement:
   Detected plate regions are cropped and enhanced for OCR.
4. OCR:
   EasyOCR is used to extract the license plate number. If it fails, Tesseract is used as a backup.
5. Violation Logging:
   If a valid plate is detected and not in cooldown, the violation is logged, an image is saved, and an email/API notification is sent.
6. Boom Barrier Control:
   The recognized plate is sent to Arduino, which opens the boom barrier, waits, and then closes it.
7. User Interface:
   The system displays the live feed with overlays, detection results, and status info. The detection line can be moved with the mouse.

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Hardware Requirements

• Camera (USB webcam or IP camera)
• Arduino (Uno/Nano) with:
  • Ultrasonic sensor (for vehicle detection)
  • Servo motor (for boom barrier)
  • LEDs (for status indication)
• Computer (Windows/Linux, with Python 3.x and a GPU for best YOLO performance)

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Getting Started

1. Clone the repository and install dependencies:

   pip install opencv-python numpy easyocr pytesseract ultralytics pyserial

2. Connect your Arduino (with ultrasonic sensor and servo) to your PC.

3. Configure serial port in the Python script (default: COM17).

4. Place YOLO model weights and Haar cascade XML in the specified paths.

5. Run the main script:

   python Final_detector.py

6. Controls:

   • 'q': Quit application
   • 'c': Clear violation history
   • Mouse drag: Move detection line
   • 'r'/'g': Simulate vehicle red/green light (keyboard mode)

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Project Structure

.
├── Arduino/
│   └── [Arduino code for sensor & boom]
├── models/
│   └── [YOLO weights]
├── violations/
│   └── [Saved violation images]
├── Final_detector.py
├── api_manager.py
└── ...

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Customization

• Detection line position: Drag with mouse in the UI.
• Cooldown time: Adjust self.violation_timeout in the code.
• API/email logic: Modify api_manager.py as needed.
• Camera index: Change in start_camera() if needed.

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Troubleshooting

• YOLO not available: Make sure model weights are in the correct path and dependencies are installed.
• Arduino not detected: Check COM port and wiring.
• No camera: Ensure your camera is connected and accessible.
• OCR errors: Try cleaning the camera lens or improving lighting.

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