  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.
