道路-移动Seg:来自移动设备的遥感图像的轻量级和准确的道路提取模型
Guangjun Qu1,2, Yue Wu2, Zhihong Lv3
1School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100020, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究介绍了Road-MobileSeg,这是一种轻量级的深度学习模型,用于从移动设备上的遥感图像中准确地提取道路. 它实现了高精度和低延迟,克服了当前复杂模型的限制.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 对于道路开采的深度学习模型是计算密集型和内存繁重的.
- 这限制了它们在资源有限的移动设备上的应用.
研究的目的:
- 为移动设备开发一种轻量级且准确的道路提取模型.
- 为了使移动平台上的遥感图像能够自动提取道路.
主要方法:
- 引入了道路移动Seg与道路移动Former的骨干.
- 集成的坐标注意模块,以提高准确性.
- 利用微型令牌金字塔模块来减少模型大小和计算.
主要成果:
- 开发了三种变体:道路移动Seg-Tiny,小,和基础.
- 与现有模型相比,实现了卓越的准确性,轻量结构和低延迟.
- 在不同资源的移动设备上表现出高性能.
结论:
- 道路-移动Seg模型有效地解决了当前道路开采技术的局限性.
- 协调注意力和微型令牌金字塔模块的集成可以实现高效的移动部署.
- 拟议的模型适用于从移动设备上的遥感数据中实时提取道路.
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