DBDoco / yolo-swimmer-detection

Swimmer detection application implemented in Python using the YOLO algorithm. The application includes a frontend served with a simple HTTP server and a Flask server that utilizes the model to detect swimmers in images or videos.

Swimmer Detection Using YOLO Algorithm

Swimmer detection application implemented in Python using the YOLO algorithm. The application includes a frontend served with a simple HTTP server and a Flask server that utilizes the model to detect swimmers in images or videos.

Seminar Work | Dataset

Features

• YOLO Algorithm: Utilizes the YOLO (You Only Look Once) algorithm for real-time object detection, specifically trained to detect swimmers.
• Interactive Frontend: The frontend interface allows users to upload images or videos and view the detection results in real-time.
• Flask Server: A Flask server processes the uploaded media and applies the YOLO model to detect swimmers, providing results back to the frontend.
• Simple HTTP Server: The application frontend is served using a simple HTTP server command for easy setup and access.
• Efficient Detection: The model is optimized for high accuracy and performance, making it suitable for real-time detection scenarios.

Requirements

• Python 3.x
• Flask Library
• YOLO Model Files

How To Use

Clone this repository

$ git clone https://github.com/DBDoco/yolo-swimmer-detection.git

Install required libraries

$ pip install -r requirements.txt

Start the Flask server

$ python flask_server.py

Start the frontend server

$ python -m http.server

Access the application by navigating to http://localhost:8000 in your web browser. After that you can upload images and videos through the UI. Processed images or videos will be saved to the 'processed' folder.