Sign Language Detection with YOLOv8

Authors

• @josemaese
• @santiagomoreno0123

Overview

This project, named "Sign Language Detection with YOLOv8," utilizes a Raspberry Pi equipped with a webcam and a Sense HAT module to detect sign language gestures. The detected signs are displayed on the Sense HAT LED matrix and also vocalized through a Bluetooth-connected speaker.

Prerequisites

Before running the project, ensure you have the following components and libraries installed:

• Raspberry Pi with Sense HAT module
• Webcam
• Bluetooth speaker
• OpenCV
• Ultralytics YOLOv8
• Python libraries: sense-hat, pygame (for sound), and other dependencies listed in the code.

Setup

1. Clone the repository:

   git clone https://github.com/JoseMaese/Sign-Language-Detection-YOLO.git
   cd Sign-Language-Detection-YOLOv8

2. Install required dependencies:

   pip install -r requirements.txt

3. Download the YOLOv8 model weights (SignLanguageModel003.pt) and place them in the project directory.

4. Connect the Raspberry Pi to the webcam and Bluetooth speaker.

Usage

Run the main script:

python sign_language_detection.py

Training results