🚗 Autonomous Vehicle Project

Welcome to the Autonomous Vehicle Project! This repository contains a modular, multi-component system for smart vehicle perception and control, developed as part of a PFA (Projet de Fin d'Année). It integrates state-of-the-art computer vision and deep learning for real-time object detection, license plate recognition, and traffic sign classification, with hardware control for a physical prototype.

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✨ Features

🚦	Traffic Sign Recognition	Classifies traffic signs using a CNN trained on the GTSRB dataset, with a user-friendly GUI.
🔍	Real-time Object Detection	Detects objects (cars, pedestrians, cyclists) using a pre-trained MobileNet SSD model.
🏷️	License Plate Recognition	Detects license plates with YOLOv8, recognizes characters with EasyOCR, and checks status in a database.
🛠️	Hardware Integration	Scripts for controlling camera, motors, servos, ultrasonic sensors, and LEDs.

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🗂️ Project Structure

.
├── LIcense Plate Number Detection/
│   ├── check_vehicle_status.py
│   ├── license_plate_recognition.py
│   ├── add_sample_vehicles.py
│   ├── db_connector.py
│   ├── Test_WebCam.py
│   └── license_plate_detector.pt
│
├── Traffic signs Detection And Recognition/
│   ├── Train.ipynb
│   ├── Test_images/
│   ├── Test/
│   │   ├── rpiCam.py
│   │   ├── interface.py
│   │   └── cameraPC.py
│   ├── model/
│   │   ├── traffic_classifiernew.h5
│   │   └── model_trained_epoch30.p
│   └── images/
│       ├── project.jpg
│       ├── predictions.png
│       └── GUI.jpg
│
├── Object Detection/
│   ├── detect.py
│   ├── MobileNetSSD_deploy.prototxt.txt
│   └── MobileNetSSD_deploy.caffemodel
│
├── Materiels_Test/
│   ├── servoMotor.py
│   ├── leds.py
│   ├── ultrasonic.py
│   ├── motor.py
│   └── Camera.py
│
├── README.md
└── requirements.txt

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🛠️ Technologies Used

• Python 3
• PyTorch (YOLOv8 for license plate detection)
• TensorFlow/Keras (traffic sign recognition)
• Caffe (object detection model)
• OpenCV (image/video processing)
• EasyOCR (license plate OCR)
• Pillow (GUI image handling)
• MongoDB (vehicle/license plate database)
• Tkinter (traffic sign classifier GUI)
• NumPy, Matplotlib, ultralytics, requests

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🚀 Quick Start

1. Clone the repository:

   git clone https://github.com/sofian2022/Projet_PFA-Autonomous_Vehicles.git
   cd Projet_PFA-Autonomous_Vehicles
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Database Setup (for License Plate Recognition):
   • Ensure MongoDB is running.
   • Update connection details in LIcense Plate Number Detection/db_connector.py if needed.
   • Populate the database with add_sample_vehicles.py if desired.

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🖥️ Usage

• Object Detection:

  cd Object Detection
  python detect.py
  

  Starts real-time object detection using your webcam.

• License Plate Recognition: Main script: license_plate_recognition.py (see script for usage details; can process images or video streams).

• Traffic Sign Classifier GUI:

  cd "Traffic signs Detection And Recognition/Test"
  python interface.py
  

  Opens a GUI for uploading and classifying traffic sign images.

• Hardware Tests: Scripts in Materiels_Test/ are for Raspberry Pi or similar hardware with the appropriate components.

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📸 Screenshots

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📄 License

This project is open-source. Please credit the original authors if you use or modify this code.