文章基本信息

标题：Infrared and Visible Airborne Targets Image Fusion with Applications to Sense and Avoid
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作者：Zhouyu Zhang ; Youmin Zhang ; Yunfeng Cao 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14742-14747
DOI：10.1016/j.ifacol.2020.12.1892
语种：English
出版社：Elsevier
摘要：AbstractMachine vision has revealed great potential in recent years for Sense and Avoid (SAA) ability of Unmanned Aerial Vehicle (UAV). However, the target perception capability of machine vision largely depends on illumination, which restricts UAV to move safely in dark environment. Since images acquired by infrared and visible sensors are complementary in most cases, enhancing image qualities in dark environments by fusion of infrared and visible images is a promising solution. By considering the difficulties of image fusion for airborne targets, a Convolutional Sparse Representation (CSR) based infrared and visible airborne targets image fusion algorithm is proposed in this paper for enhancing SAA capability of UAV in dark environments, which contains three parts: image decomposition, image transformation and image reconstruction. A series of registered infrared and visible images containing airborne targets are selected to evaluate the algorithm proposed in this paper. Simulation results demonstrate the algorithm proposed in this paper effectively increases image qualities in dark environments. In the aspects of fusion metrics, the algorithm proposed in this paper can achieve favorable performance against other image fusion algorithms.
关键词：KeywordsSenseAvoid (SAA)machine visiontarget perceptionimage fusionConvolutional Sparse Representation (CSR)