Overview:
This project involves detecting and recognizing traffic light colors using computer vision techniques. It leverages the YOLOv8l model, trained on 999 images to classify traffic lights into three categories: red, yellow, and green. The project aims to deliver accurate real-time detection for applications like traffic monitoring and autonomous driving.
Key Features:
1. YOLOv8l for Traffic Light Detection:
The project uses the YOLOv8l model to detect and classify traffic lights in real-time. The model is trained to recognize three categories of lights such as red, yellow, and green.
2. Accurate Color Classification:
The system delivers precise identification of traffic light colors, ensuring efficient performance for traffic monitoring and autonomous systems.
3. Dataset:
The model is trained on 999 traffic light images, enabling it to generalize across different lighting conditions and environments.
4. High Performance:
Achieved an impressive mean average precision (mAP) of 0.983, with precision at 0.947 and recall at 0.965, ensuring reliable detection.
Category:
Tags:
Links
Portfolio 
Leave a comment