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可见和红外无人机目标图像注册的一般跨模式注册框架

Yu Luo¹, Hao Cha¹, Lei Zuo²

¹College of Electronic Engineering, Naval University of Engineering, Wuhan, 4300000, China.

Scientific reports

|August 9, 2023

概括

本研究引入了通用跨模式注册 (GCMR) 框架,以改进无人机目标检测. 这种新的方法有效地调整了错位的可见和红外图像,在具有挑战性的条件下提高了检测准确性.

科学领域:

计算机视觉计算机视觉
遥感遥感遥感遥感
人工智能的人工智能

背景情况:

多模式图像对为图像引导无人机 (UAV) 目标检测提供了关键的补充信息.
现实世界的场景经常呈现出错位的多模式图像,由于空间变形和跨模式差异,这给图像注册带来了重大挑战.

研究的目的:

开发一个强大的跨模式图像注册框架,特别是解决可见和红外图像在无人机应用中的错位问题.
将复杂的跨模式注册简化为更易于管理的单模式注册任务.

主要方法:

引入通用跨模式注册 (GCMR) 框架,使用一代注册模式.
实现图像交叉模式翻译网络 (ICMTN),从可见图像生成伪红外图像,纠正结构扭曲.
在单一模式注册中使用多级残留密度注册网络 (MRDRN) 进行增强的特征提取和相互信息利用.

主要成果:

拟议的GCMR框架,包括ICMTN和MRDRN,在记录错位的可见和红外图像方面表现出卓越的性能.
对公共反无人机数据集的广泛实验表明,在建筑的五种评估变体中,最先进的结果得到了评估.
该方法有效地克服了与空间变形和图像注册中的跨模式差异相关的挑战.

结论:

开发的GCMR框架为无人机目标检测任务中的可见红外图像注册提供了有效的解决方案.
ICMTN和MRDRN的新组合显著提高了注册的准确性和稳定性.
这项工作推进了用于全天全天候监视和侦察应用的图像记录领域.

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