YOLO-BS: a traffic sign detection algorithm based on YOLOv8
Abstract 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 detecti...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88184-0 |
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| author | Hong Zhang Mingyin Liang Yufeng Wang |
| author_facet | Hong Zhang Mingyin Liang Yufeng Wang |
| author_sort | Hong Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-aee35d276f8e40dc962ef2c3af7cd7b8 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aee35d276f8e40dc962ef2c3af7cd7b82025-08-20T01:57:45ZengNature PortfolioScientific Reports2045-23222025-03-0115111110.1038/s41598-025-88184-0YOLO-BS: a traffic sign detection algorithm based on YOLOv8Hong Zhang0Mingyin Liang1Yufeng Wang2Transportation Institute of Inner Mongolia UniversityTransportation Institute of Inner Mongolia UniversityTransportation Institute of Inner Mongolia UniversityAbstract 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.https://doi.org/10.1038/s41598-025-88184-0Traffic sign detectionDeep learningYOLOTT100KBiFPN |
| spellingShingle | Hong Zhang Mingyin Liang Yufeng Wang YOLO-BS: a traffic sign detection algorithm based on YOLOv8 Scientific Reports Traffic sign detection Deep learning YOLO TT100K BiFPN |
| title | YOLO-BS: a traffic sign detection algorithm based on YOLOv8 |
| title_full | YOLO-BS: a traffic sign detection algorithm based on YOLOv8 |
| title_fullStr | YOLO-BS: a traffic sign detection algorithm based on YOLOv8 |
| title_full_unstemmed | YOLO-BS: a traffic sign detection algorithm based on YOLOv8 |
| title_short | YOLO-BS: a traffic sign detection algorithm based on YOLOv8 |
| title_sort | yolo bs a traffic sign detection algorithm based on yolov8 |
| topic | Traffic sign detection Deep learning YOLO TT100K BiFPN |
| url | https://doi.org/10.1038/s41598-025-88184-0 |
| work_keys_str_mv | AT hongzhang yolobsatrafficsigndetectionalgorithmbasedonyolov8 AT mingyinliang yolobsatrafficsigndetectionalgorithmbasedonyolov8 AT yufengwang yolobsatrafficsigndetectionalgorithmbasedonyolov8 |