YOLO-BS: a traffic sign detection algorithm based on YOLOv8

Traffic signs are pivotal components of traffic management, ensuring the regulation and safety of road traffic. However, existing detection methods often suffer from low accuracy and poor real-time performance in dynamic road environments. This paper reviews traditional traffic sign detection method...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 7558 - 11
Main Authors: Zhang, Hong, Liang, Mingyin, Wang, Yufeng
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 04.03.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/117
639/705/258
Algorithms
BiFPN
Deep learning
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
Traffic control
Traffic management
Traffic sign detection
TT100K
YOLO
Deep learning
YOLO
Traffic sign detection
TT100K
BiFPN
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Traffic signs are pivotal components of traffic management, ensuring the regulation and safety of road traffic. However, existing detection methods often suffer from low accuracy and poor real-time performance in dynamic road environments. This paper reviews traditional traffic sign detection methods and introduces an enhanced detection algorithm (YOLO-BS) based on YOLOv8 (You Only Look Once version 8). This algorithm addresses the challenges of complex backgrounds and small-sized detection targets in traffic sign images. A small object detection layer was incorporated into the YOLOv8 framework to enrich feature extraction. Additionally, a bidirectional feature pyramid network (BiFPN) was integrated into the detection framework to enhance the handling of multi-scale objects and improve the performance in detecting small objects. Experiments were conducted on the TT100K dataset to evaluate key metrics such as model size, recall, mean average precision (mAP), and frames per second (FPS), demonstrating that YOLO-BS surpasses current mainstream models with mAP50 of 90.1% and FPS of 78. Future work will refine YOLO-BS to explore broader applications within intelligent transportation systems.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-88184-0