一个轻量级的网络,带有双路特征增强器和双向门式融合,用于云检测
Yan Mo1,2, Puhui Chen3, Shaowei Bai2
1College of Aeronautics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究介绍了一种轻量级的网络,用于在遥感图像中高效地检测云. 该模型实现了高精度,同时显著降低了计算成本,使其适用于资源有限的应用程序.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 云检测对于遥感图像分析至关重要.
- 现有的深度学习模型通常是计算密集型的,限制了它们在边缘计算中的使用.
研究的目的:
- 为准确和高效的云检测开发一个轻量级的网络.
- 解决远程传感云检测中精度和计算成本之间的权衡问题.
主要方法:
- 引入了一种双路径特征增强器,用于多级特征提取和融合.
- 开发了一种双向封闭融合模块,具有注意力和动态卷积,用于自适应功能集成.
主要成果:
- 在HRC_WHU数据集上实现了96.31%的整体准确度和92.82%的平均交叉点.
- 证明了12.04 GFLOP的低计算成本,超过了最先进的方法.
结论:
- 拟议的轻量级网络有效地平衡了高检测性能和计算效率.
- 在高分辨率遥感图像中提供实用解决方案,用于实时轻量级云检测.
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