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