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ESA-YOLO: An efficient scale-aware traffic sign detection algorithm based on YOLOv11 under adverse weather conditions

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Publicado en:PLoS One vol. 20, no. 11 (Nov 2025), p. e0336863
Autor principal: Li, ChenHao
Otros Autores: Liu, ShuXian, Peng, ZiNuo
Publicado:
Public Library of Science
Materias:
Feature extraction
Traffic
Accuracy
Traffic signs
Deep learning
Algorithms
Weather
Weather conditions
Computer applications
Critical components
Robustness
Advanced driver assistance systems
Machine learning
Datasets
Spatial data
Computer vision
Sensors
Traffic control
Channel gating
Telematics
Complexity
Multisensor fusion
Semantics
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Traffic sign detection is a critical component of autonomous driving and advanced driver assistance systems, yet challenges persist in achieving high accuracy while maintaining efficiency, particularly for multi-scale and small objects in complex scenes. This paper proposes an improved YOLOv11-based traffic sign detection algorithm that tackles above challenges through three key innovations: (1) A Dense Multi-path Feature Pyramid Network (DMFPN) that boosts multi-scale feature fusion by enabling comprehensive bidirectional interaction between high-level semantic and low-level spatial information, augmented by a dynamic weighted fusion mechanism. (2) A Context-Aware Gating Block (CAGB) that efficiently integrates local and global contextual information through lightweight token and channel mixer, enhancing the small-object detection ability without excessive computational overhead. (3) An Adaptive Scene Perception Head (ASPH) that synergistically combines multi-scale feature extraction with attention mechanisms to improve robustness in adverse weather condition. Extensive experiments on the TT100K and CCTSDB2021 datasets demonstrate the model’s superior performance. On the TT100K dataset, our model outperforms the state-of-the-art YOLOv11n model, achieving improvements of 3.8% in mAP@50 and 3.9% in mAP@50-95 while maintaining comparable computational complexity and reducing parameters by 20%. Similar gains are observed on the CCTSDB2021 dataset, with enhancements of 2.3% in mAP@50 and 1.8% in mAP@50-95. Furthermore, experimental results also demonstrate that our proposed model exhibits superior performance in small object detection and robustness in complex environments compared to mainstream competitors.
ISSN:1932-6203
DOI:10.1371/journal.pone.0336863
Fuente:Health & Medical Collection