Building a real-time license plate detection system using YOLOv8 and EasyOCR

• Overview
• Features
• System Architecture
• Installation
• Usage
• Project Structure
• Development Progress
• Technical Details

I'm working on an ALPR system that can automatically detect and read license plates from images and videos in real-time. The goal is to create something that works well in different lighting conditions and camera angles - basically making it robust enough for real-world use.

This project tackles the challenge of automated vehicle identification, which has practical applications in parking management, traffic monitoring, and security systems.

• Real-time processing: Works with live video feeds
• Custom trained model: Built using 11,776+ CCPD dataset images for high accuracy
• Handles Chinese plates: Specialized character recognition for Chinese license plates
• Weather resistant: Performs well across different lighting and environmental conditions
• Modular design: Easy to integrate into existing systems
• Well documented: Includes detailed exploration notebooks showing the development process

The system works in stages:

1. Data Processing: I explored and converted the CCPD dataset to YOLO format
2. Detection Module: Custom YOLOv8 model finds license plates in images
3. Recognition Module: EasyOCR extracts the actual text from detected plates
4. Pipeline Integration: Everything works together seamlessly
5. User Interface: Planning a Streamlit dashboard for easy interaction

• Python 3.8 or higher
• A decent GPU helps (but not required - I optimized for CPU training too)
• At least 8GB RAM if you want to train from scratch

1. Get the code

git clone https://github.com/yourusername/alpr-system.git
cd alpr-real-time

2. Set up your environment

python -m venv alpr_env
source alpr_env/bin/activate 

3. Install everything

pip install -r requirements.txt

from src.detect_plate import LicensePlateDetector
# Setup the Detector
detector = LicensePlateDetector()

# Load the trained model
detector.load_model('models/license_plate_detection/best.pt')

# Detect plates in your image
results = detector.detect_plates('path/to/image.jpg')

jupyter notebook notebooks/01_ccpd_exploration.ipynb

• Project setup and environment configuration
• CCPD dataset exploration and analysis (11,776 images)
• Dataset conversion pipeline (CCPD → YOLO format)
• YOLOv8 training pipeline implementation
• Model training completed with 99.5% mAP50 accuracy
• CPU-optimized training configuration
• Professional project documentation

• Model Performance: 99.5% mAP50 accuracy achieved in 4 epochs
• Training Method: YOLOv8n with early stopping (patience=10)
• Dataset: 11,776 CCPD images with Chinese license plates
• Status: Production-ready model available at models/license_plate_detection/yolov8_ccpd_optimized/weights/best.pt

• OCR integration with EasyOCR
• Complete ALPR pipeline (YOLOv8 → OCR → Text extraction)
• 95% OCR confidence achieved on test license plates
• Energy-efficient testing framework with model reuse
• End-to-end license plate text recognition working

• OCR Performance: 95% confidence on synthetic test plates
• Pipeline Status: YOLOv8 detection + EasyOCR text extraction functional
• Language Support: English and Chinese character recognition
• Implementation: src/ocr_reader.py with preprocessing and error correction
• Testing: Comprehensive test suite in src/test_ocr.py
• Character Accuracy: Handles common OCR errors (O→0, I→1, S→5, Z→2)

• Video processing pipeline implementation
• Enhanced testing framework with video capabilities
• Test video generation for development and validation
• Live camera feed processing integration
• Frame-by-frame license plate detection in video streams

• Video Support: MP4 file processing with configurable frame skipping for performance
• Live Camera: Real-time webcam integration with detection visualization
• Test Generation: Synthetic video creation with moving license plates for validation
• Performance: Optimized processing every 3rd frame for real-time capability
• Implementation: Extended src/utils.py with video utilities and enhanced src/test_ocr.py
• Processing Results: Successfully detecting and reading license plates in video sequences

• Streamlit dashboard interface development
• Real-time video stream integration with web interface
• Detection history and logging system
• Performance metrics dashboard
• User interface for video upload and processing