farukalamai/traffic-lights-detection-and-color-recognition-using-yolov8

traffic-lights-detection-and-color-recognition-using-yolov8

Traffic Lights Detection and Color Recognition using YOLOv8

Introduction

This project is a computer vision application that utilizes the YOLOv8 deep learning model to detect traffic lights in images and recognize their colors. It can be useful in various traffic management and autonomous driving scenarios.

The YOLOv8 model is a state-of-the-art object detection model that provides real-time object detection and classification. In this project, it has been trained specifically for detecting traffic lights and distinguishing between red, yellow, and green lights.

Features

• Detects traffic lights in images and provides bounding box coordinates.
• Recognizes the color of the detected traffic lights (red, yellow, or green).
• Provides high accuracy and real-time performance.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.7 or higher.
• NVIDIA GPU (recommended for faster inference and training).
• CUDA and cuDNN (if using GPU).
• Git (for cloning the repository).

Installation

1. Clone the repository:

   git clone https://github.com/farukalampro/traffic-lights-detection-and-color-recognition-using-yolov8.git
   cd traffic-lights-detection-and-color-recognition-using-yolov8