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.

本文使用了改进的 YOLOv5 和 YOLOv8 模型，在不同照明条件下检测和分类不同的道路标志，实验结果表明，两个模型在不同训练设置下都能表现良好，其中 YOLOv8 模型的 MAP50 得分略高。这些发现为寻求可靠且适应性强的物体检测应用方案的从业人员提供了宝贵的见解。