Advancing Roadway Sign Detection with YOLO Models and Transfer Learning

被引：2

作者：

Nafaa, Selvia ^{[1
]}

Ashour, Karim ^{[1
]}

Mohamed, Rana ^{[1
]}

Essam, Hafsa ^{[1
]}

Emad, Doaa ^{[1
]}

Elhenawy, Mohammed ^{[2
]}

Ashqar, Huthaifa I. ^{[3
]}

Hassan, Abdallah A. ^{[1
]}

Alhadidi, Taqwa I. ^{[4
]}

机构：

[1] Minia Univ, Comp & Syst Dept, Al Minya, Egypt

[2] Queensland Univ Technol, CARRS Q, Brisbane, Qld, Australia

[3] Arab Amer Univ, Civil Engn Dept, Jenin, Palestine

[4] Al Ahliyya Amman Univ, Civil Engn Dept, Amman, Jordan

来源：

2024 IEEE 3RD INTERNATIONAL CONFERENCE ON COMPUTING AND MACHINE INTELLIGENCE, ICMI 2024 | 2024年

关键词：

Deep Learning; Assets Management; Signs Detection;

D O I：

10.1109/ICMI60790.2024.10586105

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：4