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

Yang WANG ¹Hongguang LI ¹Xinjun LI ¹Zhipeng WANG ²Baochang ZHANG³

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作者信息

1. Institute of Unmanned System,Beihang University,Beijing 100191,China
2. School of Electronics and Information Engineering,Beihang University,Beijing 100191,China
3. Institute of Artificial Intelligence,Beihang University,Beijing 100191,China
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Abstract

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.

Key words

Object localization/Deep learning/Depth estimate/Scale recovery/Unmanned Aerial Vehicle(UAV)

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基金项目

National Key Research and Development Program of China(2022YFB3904303)

National Key Research and Development Program of China(2020YFB0505602)

National Natural Science Foundation of China(62076019)

National Natural Science Foundation of China(62022012)

National Natural Science Foundation of China(U2233217)

National Natural Science Foundation of China(62101019)

National Natural Science Foundation of China(62371029)

Civil Aviation Security Capacity Building Fund Project,China(CAAC Contract 2020123)

Civil Aviation Security Capacity Building Fund Project,China(CAAC Contract 202177)

Civil Aviation Security Capacity Building Fund Project,China(CAAC Contract 2022110)

出版年

2024

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

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