Dangerous Sign Recognition Algorithm Based on YOLOv8

Authors

Yifan Han School of Computer Science, Beijing University of Information Science and Technology, Beijing 102206, China
Huatian Li School of Computer Science, Beijing University of Information Science and Technology, Beijing 102206, China
Junyi Zhu School of Computer Science, Beijing University of Information Science and Technology, Beijing 102206, China
Shuyu Xiao School of Computer Science, Beijing University of Information Science and Technology, Beijing 102206, China
Pengyue Si School of Computer Science, Beijing University of Information Science and Technology, Beijing 102206, China

DOI:

https://doi.org/10.53469/wjimt.2025.08(09).02

Keywords:

Object detection, Deep learning, YOLOv8, Danger signs

Abstract

With the continuous advancement of deep learning technology, object detection, as a key technology in the field of computer vision, has been widely applied in multiple fields such as video surveillance, intelligent transportation, and autonomous driving. As a classic object detection algorithm, You Only Look Once (YOLO) has been highly valued by researchers since YOLOv1 for its efficient detection speed and good real-time performance. Research was conducted on the latest version of YOLOv8 algorithm, analyzing its network structure optimization and performance improvement. Utilizing YOLOv8's real-time object detection capability, efficient recognition of various danger signs was achieved through deep learning of their features. This provides an effective solution for real-time danger sign recognition and has significant practical implications for assisting post disaster rescue robots in rescue operations.

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Dangerous Sign Recognition Algorithm Based on YOLOv8