Advancing Roadway Sign Detection with YOLO Models and Transfer Learning

TL;DR

This paper improves roadway sign detection by modifying YOLOv5 and YOLOv8 models, demonstrating high accuracy under various training conditions, which benefits advanced driving systems.

Contribution

It introduces modifications to YOLOv5 and YOLOv8 for better roadway sign detection and evaluates their performance across different training setups.

Findings

YOLOv8 achieves MAP50 scores up to 97.1%.

Both models perform reliably under various training conditions.

YOLOv8 generally outperforms YOLOv5 in accuracy.

Abstract

Roadway signs detection and recognition is an essential element in the Advanced Driving Assistant Systems (ADAS). Several artificial intelligence methods have been used widely among of them YOLOv5 and YOLOv8. In this paper, we used a modified YOLOv5 and YOLOv8 to detect and classify different roadway signs under different illumination conditions. Experimental results indicated that for the YOLOv8 model, varying the number of epochs and batch size yields consistent MAP50 scores, ranging from 94.6% to 97.1% on the testing set. The YOLOv5 model demonstrates competitive performance, with MAP50 scores ranging from 92.4% to 96.9%. These results suggest that both models perform well across different training setups, with YOLOv8 generally achieving slightly higher MAP50 scores. These findings suggest that both models can perform well under different training setups, offering valuable insights for practitioners seeking reliable and adaptable solutions in object detection applications.