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Pothole Detection 🚗💥

This project uses YOLO (You Only Look Once) 🧠 for detecting potholes in road surfaces using computer vision. The model analyzes images and videos to identify potholes, helping improve road safety 🚧, support infrastructure maintenance 🛠️, and enhance autonomous vehicle navigation 🚙.

Features ✨

  • Real-time pothole detection on images and video streams 🎥📸.
  • Pretrained YOLO models (YOLOv5, YOLOv8) for accurate and fast pothole detection ⚡.
  • Custom pothole detection dataset built from real-world images to ensure diverse conditions 🛣️

Technologies 💻

  • Languages: Python 🐍

  • Libraries:

    • OpenCV (cv2): For image processing and video stream analysis 🎥.
    • PyTorch (torch): Deep learning framework used for training and inference 💻.
    • ElementTree (xml.etree.ElementTree): For parsing XML data 📑.
    • YAML: For handling configuration files 📄.
    • OS and Shutil: For file and directory manipulation ⚙️.

    The core object detection is done using YOLO (v5, v8) 🔍.

  • Notebook: The main work is done in the Jupyter Notebook: Potholes_image_Detection.ipynb

Installation ⚙️

  1. Clone the repository: 
    git clone https://github.com/Ali-EL-Badry/Pothole-Detection.git