YOLO-Night:为拥有强大的夜间感知能力的自动驾驶汽车照明道路
Jinxin Tian1, Muhammad Arslan Ghaffar2,3, Zhaokai Li4
1Henan Mechanical and Electrical Vocational College, Zhengzhou 451191, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
YOLO-Night增强了对象检测,用于在低光下进行自动驾驶. 这一新框架显著提高了夜间驾驶场景的精度和回忆.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主系统 自主系统
背景情况:
- 由于噪音和可见度差,物体检测系统在低光条件下扎.
- 现有的YOLO模型是为白天优化的,晚上表现不佳.
研究的目的:
- 开发一个物体检测框架,YOLO-Night,专门用于夜间自动驾驶.
- 为了增强YOLO11架构,以提高低光性能.
主要方法:
- 集成的对比增强和自适应的受感场建模.
- 采用深度可切换的心脏卷积用于动态受体场.
- 使用了多级特征融合与辅助低级检测头.
主要成果:
- 在NightCity数据集中,YOLO-Night的表现优于YOLO11n的基线.
- 在夜间条件下,精度为+14.3%,回忆率为+12.4%,mAP@50改进率为+10.4%.
- 保持了实时推断能力.
结论:
- 有针对性的架构调整大大提高了低光驾驶中对象检测的稳定性.
- YOLO-Night为可靠的夜间自动驾驶感知提供了可行的解决方案.
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