驾驶场景图像 基于多分支和多尺度特征融合的脱雾模型
1Institute of Artificial Intelligence & Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, PR China.
概括
这项研究引入了一种新的图像消毒模型,用于自动驾驶和监控中的清晰视觉效果. 高效的网络通过融合空间和频率信息,有效地消除雾,增强实时感知.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 图像dehazing对于提高视觉质量至关重要,如自动驾驶和监控等关键应用程序至关重要.
- 现有的方法往往难以处理复杂的场景和实时处理需求.
研究的目的:
- 开发一个创新的和高效的图像dehazing模型,以提高视觉感知.
- 解决当前除尘技术的局限性,特别是对于现实驾驶场景.
主要方法:
- 一个多分支和多尺度的特征融合网络,将本地和全球特征结合起来,使用深度可分离的卷积和状态空间模型 (Mamba).
- 通过卷积层和里埃变换集成空间和频域信息.
- 一个特征融合机制,使用通道注意力和残余连接来进行动态特征加权.
主要成果:
- 拟议的模型在真实世界雾驾驶数据集上实现了卓越的排气性能.
- 显示了高处理效率,率为32.41 FPS和67.96 MPx/s.
- 成功地将Mamba和卷积结合起来,在驾驶场景中有效地提取全局特征.
结论:
- 这种新型模型提供了可靠的视觉感知和在复杂环境中改进的适应性.
- 空间,频率和先进网络架构的融合使得有效和高效的雾消除成为可能.
- 这项工作代表了自主系统实时图像处理的重大进步.
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