License Plate Detection

Introduction

Welcome to the License Plate Detection Software, meticulously crafted by Abdullah Alshateri and Ahmed Balfiqah, both third-year Electronic and Computer Engineering students at UTM, during their internship in Smart Navigation System in the UAE.

Our software leverages the formidable capabilities of the Ultralytics YOLOv8 model to deliver lightning-fast license plate detection using the ESP32-CAM. But we don't stop at mere detection! With advanced machine learning algorithms, the software delves deep into each detection, deciphering even the tiniest nuances of a license plate. Moreover, it seamlessly integrates these detections into an Excel database, ensuring that data archival is not just efficient but also organized.

Whether you're an enthusiast looking for a reliable license plate recognition solution or a professional seeking robust software for larger systems, our tool is designed to impress and deliver. Dive in and discover our license plate detector!

License Plate Detection Model Update (2023-09-06):

• Stability: The system is now in a stable state.

• Accuracy: Its detection accuracy is outstanding, exceeding 90%. Failures are rare.

• Output Details: The model can discern and provide:

  • Plate category ✅
  • Plate number ✅
  • Plate state ✅

The model’s detection ability is parallel to human eyesight:

• If it recognizes a plate, it captures it with high precision.
• When the model cannot detect a plate, it will clearly mention that no plate could be detected.

Table of Contents

• System Flow
• Dependencies
• User Guide
• Updating the Repository

Tips to avoid problems:

1. Ensure all dependencies are installed.
2. Clone the repository.
3. Run the plate-detector.py Python script.
4. Input image URLs when prompted.
5. View the detected license plates and their details.
6. All detected details will be saved in an Excel file.

Dependencies

Before running this project, make sure to install the following Python dependencies:

• os: For OS-level operations.

  pip install os
  

• ultralytics: Used for YOLO model integrations.

  pip install ultralytics
  

• cv2: Image and video processing tasks.

  pip install opencv-python
  

• requests: To fetch images from URLs.

  pip install requests
  

• numpy: Numerical operations.

  pip install numpy
  

• io: Core I/O functionalities.

  pip install io
  

• matplotlib: Display images.

  pip install matplotlib
  

• pandas: Manage Excel operations effectively.

  pip install pandas
  

User Guide

1. Clone the Repository Use the following command to clone the repository to your local machine:

   git clone https://github.com/3bdul1ah/UAE-License-Plates-Detector.git
   

2. Navigate to the directory Change to the cloned directory using the command:

   cd UAE-License-Plate-Detector  
   

3. Setting up in an IDE After navigating to the project directory:
   • Open the folder using your preferred Integrated Development Environment (IDE) such as PyCharm, VSCode, etc.
   • Locate the plate-detector.pyscript within your IDE.
   • Execute this script to initiate the license plate detection process.

Note: Ensure you have all the necessary dependencies installed before running the script.

Updating the Repository

If you're collaborating on the UAE-License-Plates-Detector project, please adhere to the following guidelines to update the code in the repository.

1. Navigate to the Project Directory

Open your terminal and ensure you're in the project's directory. You can use the following command:

cd path/to/UAE-License-Plates-Detector

Note: Replace path/to with the actual path to your local copy of the repository.

2. Check the Status of Your Local Repository

To view which files have changed, use the command:

git status

3. Stage Your Changes

• To stage all changes, use:

  git add .

• To stage specific files, use:

  git add filename1 filename2

  Note: Replace filename1, filename2, etc. with the actual names of the files you've changed.

4. Commit Your Changes

Commit your staged changes with a descriptive message using the following command:

git commit -m "Brief description of your changes"

5. Push Your Changes to GitHub

Push your committed changes to the main repository with:

git push origin master

Note: If you're working on a branch other than master, replace master with your branch's name.

6. Stay Updated

Always make sure you have the latest changes from the main repository before beginning your work to avoid conflicts. Use the command:

git pull origin master

#Feture updates for the project:

• Updating the AI model: Using yolov11 for better results.
• Creating a smaller model: Using tiny model instead of nano for more enhanced results when raspberry pi is used.