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Pothole Detection

Overview

This project implements an advanced pothole detection system using computer vision and deep learning techniques. By leveraging the YOLOv8-small model, we've created a robust and efficient solution for identifying and localizing potholes in road images and videos.

Demo

demo.mp4

Key Features

  • YOLOv8-small Model: Utilizes the compact yet powerful YOLOv8-small architecture for real-time object detection and segmentation.
  • Multi-format Input: Processes both images and videos for versatile application.
  • Real-time Detection: Achieves fast inference times, suitable for mobile and edge devices.
  • User-friendly Interface: Implemented with Streamlit for easy interaction.

Technology Stack

  • Deep Learning Framework: YOLO (You Only Look Once) v8
  • Computer Vision: OpenCV and Supervision
  • Data Processing: NumPy
  • UI: Streamlit

Setup and Installation

  1. Clone the repository:

    git clone https://github.com/Wydoinn/Pothole-Detection.git
cd pothole-detection

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Run the Streamlit app:

    streamlit run app.py

Usage

  • Use the Streamlit app to upload images or videos for pothole detection.
  • Adjust confidence thresholds and other parameters as needed.