在雾天气中通过多级融合网络估计高速公路的可见性
Pengfei Xiao1,2, Zhendong Zhang1,2, Xiaochun Luo1,2
1Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing 210019, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究引入了一种新的深度学习模型,用于从监控图像中估计道路可见性,从而提高交通安全. 新型网络准确评估可见性,性能优于当前的方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 运输安全运输安全
背景情况:
- 道路能见度差严重影响交通安全和事故发生率.
- 传统的可见度监测缺乏现代交通需求所需的时间和空间准确性.
研究的目的:
- 开发一种新的深度网络架构,从高速公路监控图像直接估计可见度.
- 通过更准确和及时的可见性评估,提高道路安全.
主要方法:
- 利用多种不同的图像特征提取技术 (结构,光谱,场景深度).
- 设计了一个用于自适应特征提取和融合的多尺度融合网络.
- 创建了一个现实场景数据集用于培训和评估拟议的模型.
主要成果:
- 拟议的深度网络架构有效地估计了监控图像的可见性.
- 实验结果显示,与现有的可见度估计方法相比,性能优越.
- 该模型在捕捉与可见性相关的详细特征方面表现出精确性.
结论:
- 新的深度网络在估计道路可见性方面取得了重大进展.
- 这种方法提供了一种更准确,更可靠的可见度监测方法,有助于提高道路安全.
- 开发的真实场景数据集支持对可见度估计的进一步研究.
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