通过自适应地整合事件和域进行夜间交通对象检测
Yu Jiang1,2, Yuehang Wang1,2, Minghao Zhao3
1The College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Fundamental research
|August 8, 2025
概括
事件摄像机通过克服RGB摄像机在低光条件下的限制来增强夜间交通物体检测. 我们的自适应融合方法提高了智能运输系统的性能.
科学领域:
- 计算机视觉 计算机视觉
- 智能运输系统 (ITS) 是一种智能运输系统.
- 传感器融合式传感器
背景情况:
- 传统的RGB摄像机由于有限的动态范围而难以处理夜间交通场景.
- 事件摄像机在低光条件下提供卓越的性能,因为它们的动态范围很高.
- 多模式学习在视觉任务中显示出前景,激励了综合传感器方法.
研究的目的:
- 开发一种适应性方法,用于用于夜间交通的事件和RGB摄像头数据的联合物体检测.
- 为了有效地解决来自不同传感器模式的数据融合方面的挑战.
- 为了提高夜间交通物体检测的稳定性和准确性.
主要方法:
- 提出了一个适应性选择和融合模块,通过通道智能特征排名来实现高效的多式联通数据融合.
- 开发了一个多层次的特征金字塔网络,结合了多式联络注意力融合.
- 策划了一套新的夜间交通场景数据集,与同步的RGB和事件流同步.
主要成果:
- 拟议的方法在基于事件,基于框架和基于融合的检测方面明显优于现有的最先进技术.
- 与单模式方法相比,在检测夜间交通对象方面表现优异.
- 验证了自适应融合战略和多式联络注意力机制的有效性.
结论:
- 通过自适应融合整合事件和RGB摄像头数据,大大提高了夜间交通物体检测.
- 开发的方法为在具有挑战性的低光条件下运行的智能运输系统提供了强大的解决方案.
- 这项工作突显了多式联运传感在提高交通安全和效率方面的潜力.
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