ESA-YOLO: An efficient scale-aware traffic sign detection algorithm based on YOLOv11 under adverse weather conditions
ChenHao Li, ShuXian Liu, ZiNuo Peng
TL;DR
This paper introduces ESA-YOLO, an improved traffic sign detection algorithm that performs better in complex and adverse weather conditions while maintaining efficiency.
Contribution
The paper proposes three novel components: DMFPN, CAGB, and ASPH to enhance multi-scale detection and robustness in traffic sign recognition.
Findings
ESA-YOLO outperforms YOLOv11n by 3.8% in mAP@50 and 3.9% in mAP@50-95 on the TT100K dataset.
The model achieves 2.3% and 1.8% improvements in mAP@50 and mAP@50-95 on the CCTSDB2021 dataset.
ESA-YOLO shows superior small-object detection and robustness in adverse weather conditions.
Abstract
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)…
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Taxonomy
TopicsAdvanced Neural Network Applications · Fire Detection and Safety Systems · Multimodal Machine Learning Applications