ESA-YOLO:在恶劣天气条件下,基于YOLOv11的高效规模感知交通信号检测算法
ChenHao Li1, ShuXian Liu1, ZiNuo Peng1
1School of Computer Science and Technology, XinJiang University, Urumqi City, Xinjiang Autonomous Region, China.
PloS one
|November 14, 2025
概括
本研究引入了用于交通标志检测的增强YOLOv11算法,在复杂的驾驶场景中显著提高了多尺度和小物体的准确性和效率. 新型车在恶劣条件下表现出色,为自动驾驶系统提供了更强大的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主驾驶系统 自主驾驶系统
背景情况:
- 交通标志检测对于自动驾驶和驾驶辅助系统至关重要.
- 现有的方法难以处理多尺度,小物体和复杂/不利的条件.
- 高精度和高效率仍然是关键挑战.
研究的目的:
- 提出一个改进的基于YOLOv11的算法,用于增强交通标志检测.
- 解决探测多尺度,小物体的挑战,并提高复杂场景中的稳定性.
- 为了实现具有可比计算复杂性的优越性能.
主要方法:
- 开发了一种密集的多路径特征金字塔网络 (DMFPN),用于有效的多尺度特征融合.
- 引入了上下文意识门区块 (CAGB),以整合本地和全球的环境,用于小物体检测.
- 实现了自适应场景感知头 (ASPH),结合了多个尺度的功能和对强度的关注.
主要成果:
- 在TT100K数据集上表现优于最先进的YOLOv11n (3.8% mAP@50,3.9% mAP@50-95).
- 在CCTSDB2021数据集 (2.3% mAP@50,1.8% mAP@50-95) 上取得了显著的收益.
- 在具有降低参数 (20%) 的复杂环境中证明了优越的小物体检测和稳定性.
结论:
- 拟议的YOLOv11改进为交通标志检测提供了更准确,更有效的解决方案.
- DMFPN,CAGB和ASPH组件有效地提高了多尺度,小物体检测和稳定性.
- 该模型为自动驾驶和驾驶辅助系统提供了一个有希望的进步.
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