Pothole Detection Using YOLOv8 🚧

This project implements a machine learning solution for pothole detection using the YOLOv8 object detection model. The web application allows users to upload videos or images of roads and outputs processed files highlighting detected potholes. Designed for road safety enhancement, the system leverages computer vision to identify hazards effectively.

Features:

• YOLOv8 Integration: High-accuracy object detection for pothole identification.
• Single-Page Web Application: User-friendly interface built with Flask.
• Supports Multiple Formats: Works with both video and image inputs.
• Responsive Design: Interactive and mobile-friendly frontend using HTML and CSS.
• Automated Processing: Displays and processes uploaded files directly on the web app.

Tech Stack:

• Backend: Flask, Python
• Frontend: HTML, CSS
• Machine Learning: YOLOv8 model
• Other Tools: OpenCV for video and image processing

Usage:

1. Clone the repository:

   git clone https://github.com/your-username/pothole-detection.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask app:

   python app.py

4. Open http://127.0.0.1:5000/ in your browser and upload a file to detect potholes.

Applications:

• Road safety assessment

• Automated road condition monitoring

• Smart city infrastructure

  

  

System Overview Posters:

Poster 1:

Poster 2: