model/Road-Anomaly-Detection/web interface.md

Road Anomaly Detection Web Interface

A simple Flask web application for detecting road defects using a YOLOv8 model.

Features

• Upload images for road defect detection
• View detection results with bounding boxes
• See detection details (defect type, confidence)

Setup Instructions

1. Clone the repository

2. Install dependencies

   pip install -r requirements.txt
   

3. Ensure your model file is available

   Make sure your YOLOv8_Small_RDD.pt file is in the root directory, or update the MODEL_PATH in app.py to point to the correct location.

4. Run the application

   python app.py
   

5. Access the web interface

   Open your browser and navigate to http://localhost:5000

Project Structure

.
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── YOLOv8_Small_RDD.pt    # Your trained model file
├── static/
│   ├── css/
│   │   └── style.css      # CSS styles
│   ├── uploads/           # Uploaded images (auto-created)
│   └── results/           # Detection result images (auto-created)
└── templates/
    ├── index.html         # Homepage template
    └── results.html       # Results page template

Usage

1. Open the web interface in your browser
2. Upload a road image using the provided form
3. The system will process the image and display the results
4. View the original image, detected anomalies, and detection details