Статья

Advancing Roadway Sign Detection with YOLO Models and Transfer Learning

Selvia NafaaMinia University,Computers and Systems Department,Minia,EgyptKarim AshourMinia University,Computers and Systems Department,Minia,EgyptRana MohamedMinia University,Computers and Systems Department,Minia,EgyptHafsa EssamMinia University,Computers and Systems Department,Minia,EgyptDoaa EmadMinia University,Computers and Systems Department,Minia,EgyptMohammed ElhenawyQueensland University of Technology,CARRS-Q,Brisbane,AustraliaHuthaifa I. AshqarAl-Ahliyya Amman University,Civil Engineering Department,Amman,JordanAbdallah A. HassanMinia University,Computers and Systems Department,Minia,EgyptTaqwa I. AlhadidiAl-Ahliyya Amman University,Civil Engineering Department,Amman,Jordan

2024en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.1109/icmi60790.2024.10586105

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar