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 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 (13,83

SUBMITTER: Wang C 

PROVIDER: S-EPMC12009977 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature