维利:一种基于车辆的高效低光图像增强方法,用于智能汽车
Linwei Ye1,2, Dong Wang1,2, Dongyi Yang1,2
1Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool L69 3BX, UK.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
这项研究介绍了VELIE,这是一个用于增强低光驾驶图像的深度学习网络. 它在具有挑战性的条件下改善了对象检测和感知,提供了一个具有成本效益的实时解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 对于高级驾驶辅助系统 (ADAS) 和自动驾驶系统 (ADS) 来说,RGB摄像头至关重要,但在低光条件下很难使用.
- 现有的解决方案,如多传感器融合或专用摄像机,成本昂贵,阻碍了广泛采用.
- 目前的低光图像增强方法缺乏细节,实时能力和边缘部署适合驾驶场景.
研究的目的:
- 开发一个高效和成本效益的深度学习网络,用于增强汽车应用中的低光图像.
- 为了解决现有方法的局限性,对夜间驾驶数据集进行细节增强和实时处理.
- 为了使ADAS和ADS在不利的照明下能够进行强大的环境感知.
主要方法:
- 利用了斯温视觉变压器架构.
- 集成了马转换与U-Net,用于脱增强低光输入.
- 开发了一个深度学习增强网络,命名为基于车辆的高效低光图像增强 (VELIE).
主要成果:
- 在各种驾驶数据集上,VELIE实现了最先进的性能.
- 在低光条件下显著增强了高维环境感知任务.
- 实现了0.19秒的处理时间,使实时推断成为可能.
结论:
- 维利为汽车系统的低光图像增强提供了经济有效的解决方案.
- 拟议的网络显著提高了ADAS和ADS在复杂环境中的稳定性和性能.
- VELIE促进了边缘部署和实时处理,为更安全的自动驾驶铺平了道路.
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