对于恶劣天气领域的RFGLNet,使用频率低级增强的通用语义细分
Xin Ye1, Xiaoqi Shi2, Yuxue Li3
1Xi'an Technological University, Xi'an, China.
Scientific reports
|February 10, 2026
概括
本研究介绍了RFGLNet,这是一个在恶劣天气下语义细分的新模型. 它在雨和雾等具有挑战性的条件下实现了高精度,这对于自动驾驶至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 在恶劣天气下,语义细分是具有挑战性的,因为可见度和噪声差.
- 现有的方法在严峻的条件下与对象细节和全球背景作斗争.
- 域泛化 (DG) 旨在在未见的环境中提高模型的稳定性.
研究的目的:
- 开发一个域普用语义细分模型,以在恶劣天气下提供强大的性能.
- 在具有挑战性的条件下增强对象细节和全局结构的捕获.
- 为了使自动驾驶系统能够可靠地感知.
主要方法:
- 引入了RFGLNet,结合了SVD初始化的低级模块,富里埃增强的频道注意力和分组建模空间注意力.
- 通过富里埃变换利用频率域信息来改善全球感知.
- 采用单数值分解 (SVD) 进行高效的参数微调.
主要成果:
- 在ACDC恶劣天气数据集中,RFGLNet实现了78.3%的工会 (mIoU) 的平均交叉点.
- 该模型在具有挑战性的条件下显示出更好的细分精度.
- RFGLNet只需要432万个可训练的参数,这表明参数的效率.
结论:
- 在恶劣的天气条件下,RFGLNet提供了一个强大的解决方案,用于域泛化语义细分.
- 拟议的模块有效地增强了全球和本地特征提取.
- 该模型在提高自动驾驶的安全性和可靠性方面显示出重大前景.
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