UAV image target localization method based on outlier filter and frame buffer

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外文摘要：With rapid development of UAV technology,research on UAV image analysis has gained attention.As the existing techniques of UAV target localization often rely on additional equipment,a method of UAV target localization based on depth estimation has been proposed.However,the unique perspective of UAVs poses challenges such as the significant field of view variations and the presence of dynamic objects in the scene.As a result,the existing methods of depth estimation and scale recovery cannot be directly applied to UAV perspectives.Additionally,there is a scarcity of depth estimation datasets tailored for UAV perspectives,which makes supervised algorithms impractical.To address these issues,an outlier filter is introduced to enhance the applicability of depth estimation networks to target localization.A frame buffer method is proposed to achieve more accurate scale recovery,so as to handle complex scene textures in UAV images.The proposed method demonstrates a 14.29％improvement over the baseline.Compared with the average recovery results from UAV perspectives,the difference is only 0.88％,approaching the performance of scale recovery using ground truth labels.Furthermore,to overcome the limited availability of traditional UAV depth datasets,a method for generating depth labels from video sequences is proposed.Com-pared to state-of-the-art methods,the proposed approach achieves higher accuracy in depth estima-tion and stands for the first attempt at target localization using image sequences.Proposed algorithm and dataset are available at https://github.com/uav-tan/uav-object-localization.

外文关键词：

Object localizationDeep learningDepth estimateScale recoveryUnmanned Aerial Vehicle(UAV)

作者：

Yang WANG、Hongguang LI、Xinjun LI、Zhipeng WANG、Baochang ZHANG

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作者单位：

Institute of Unmanned System,Beihang University,Beijing 100191,China

School of Electronics and Information Engineering,Beihang University,Beijing 100191,China

Institute of Artificial Intelligence,Beihang University,Beijing 100191,China

基金：

National Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaCivil Aviation Security Capacity Building Fund Project,ChinaCivil Aviation Security Capacity Building Fund Project,ChinaCivil Aviation Security Capacity Building Fund Project,China

项目编号：

2022YFB39043032020YFB05056026207601962022012U22332176210101962371029CAAC Contract 2020123CAAC Contract 202177CAAC Contract 2022110

出版年：

2024

DOI：

10.1016/j.cja.2024.02.014

中国航空学报(英文版)

中国航空学会

中国航空学报(英文版)

CSTPCDEI

影响因子：0.847

ISSN：1000-9361

年,卷(期)：2024.37(7)