Efficient traffic sign recognition using YOLO for intelligent transport systems
Abstract Accurate traffic sign recognition (TSR) is critical for enhancing the safety and reliability of autonomous driving systems. This study proposes an optimized YOLOv5-based framework to address challenges such as small-scale detection, environmental variability, and real-time processing constr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-98111-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850139234368749568 |
|---|---|
| author | Cong Wang Bin Zheng Chenxing Li |
| author_facet | Cong Wang Bin Zheng Chenxing Li |
| author_sort | Cong Wang |
| collection | DOAJ |
| description | Abstract Accurate traffic sign recognition (TSR) is critical for enhancing the safety and reliability of autonomous driving systems. This study proposes an optimized YOLOv5-based framework to address challenges such as small-scale detection, environmental variability, and real-time processing constraints. Three key innovations are introduced: (1) k-means++ clustering for anchor box optimization, achieving a 77.55% average IoU (vs. 75.95% for traditional k-means) to enhance small-target detection; (2) comprehensive comparative analysis of YOLOv5 variants (s/m/x), revealing precision-speed trade-offs (99.3–99.5% mAP@0.5 vs. 32–45 ms inference time) for deployment flexibility; and (3) systematic hyperparameter tuning to maximize robustness across diverse scenarios. Leveraging the CCTSDB dataset (13,830 annotated images), experiments demonstrate the framework’s superiority: it attains 98.1% mean average precision (mAP), 98.6% recall, and 99.3% precision, outperforming Faster-RCNN and SSD by 5–8% in mAP while maintaining 45 FPS throughput. The YOLOv5s variant achieves optimal balance with 99.3% mAP@0.5 and 32 ms per-image inference, validated through rigorous statistical analysis (Tukey HSD). Robust performance in challenging conditions (e.g., small sample, backlit sample, foggy scenes) is evidenced by detection confidence exceeding 0.90. These results highlight the framework’s applicability in latency-sensitive intelligent transportation systems. |
| format | Article |
| id | doaj-art-aef0c62488b249e590e00634f10e38fe |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aef0c62488b249e590e00634f10e38fe2025-08-20T02:30:23ZengNature PortfolioScientific Reports2045-23222025-04-0115112210.1038/s41598-025-98111-yEfficient traffic sign recognition using YOLO for intelligent transport systemsCong Wang0Bin Zheng1Chenxing Li2School of Information and Electrical Engineering, Panzhihua UniversitySchool of Intelligent Manufacturing, Panzhihua UniversitySchool of Mathematics and Computers Big Data Science, Panzhihua UniversityAbstract Accurate traffic sign recognition (TSR) is critical for enhancing the safety and reliability of autonomous driving systems. This study proposes an optimized YOLOv5-based framework to address challenges such as small-scale detection, environmental variability, and real-time processing constraints. Three key innovations are introduced: (1) k-means++ clustering for anchor box optimization, achieving a 77.55% average IoU (vs. 75.95% for traditional k-means) to enhance small-target detection; (2) comprehensive comparative analysis of YOLOv5 variants (s/m/x), revealing precision-speed trade-offs (99.3–99.5% mAP@0.5 vs. 32–45 ms inference time) for deployment flexibility; and (3) systematic hyperparameter tuning to maximize robustness across diverse scenarios. Leveraging the CCTSDB dataset (13,830 annotated images), experiments demonstrate the framework’s superiority: it attains 98.1% mean average precision (mAP), 98.6% recall, and 99.3% precision, outperforming Faster-RCNN and SSD by 5–8% in mAP while maintaining 45 FPS throughput. The YOLOv5s variant achieves optimal balance with 99.3% mAP@0.5 and 32 ms per-image inference, validated through rigorous statistical analysis (Tukey HSD). Robust performance in challenging conditions (e.g., small sample, backlit sample, foggy scenes) is evidenced by detection confidence exceeding 0.90. These results highlight the framework’s applicability in latency-sensitive intelligent transportation systems.https://doi.org/10.1038/s41598-025-98111-yTraffic sign recognitionDeep learningTarget detection |
| spellingShingle | Cong Wang Bin Zheng Chenxing Li Efficient traffic sign recognition using YOLO for intelligent transport systems Scientific Reports Traffic sign recognition Deep learning Target detection |
| title | Efficient traffic sign recognition using YOLO for intelligent transport systems |
| title_full | Efficient traffic sign recognition using YOLO for intelligent transport systems |
| title_fullStr | Efficient traffic sign recognition using YOLO for intelligent transport systems |
| title_full_unstemmed | Efficient traffic sign recognition using YOLO for intelligent transport systems |
| title_short | Efficient traffic sign recognition using YOLO for intelligent transport systems |
| title_sort | efficient traffic sign recognition using yolo for intelligent transport systems |
| topic | Traffic sign recognition Deep learning Target detection |
| url | https://doi.org/10.1038/s41598-025-98111-y |
| work_keys_str_mv | AT congwang efficienttrafficsignrecognitionusingyoloforintelligenttransportsystems AT binzheng efficienttrafficsignrecognitionusingyoloforintelligenttransportsystems AT chenxingli efficienttrafficsignrecognitionusingyoloforintelligenttransportsystems |