基于深度学习的无人机搜救方法研究进展

doi:10.7527/S1000-6893.2025.32761

Abstract

Abstract:

With the development of artificial intelligence technology， Unmanned Aerial Vehicles （UAVs） can automatically identify and locate objects of interest by equipping different optoelectronic payloads， and are used for Search and Rescue （SaR） missions in disaster environments. UAV search and rescue missions require the rapid location of missing persons in complex and dangerous environments， facing challenges such as single detection technique， susceptibility to interference during imaging， complex background， and limited UAV platform resources. In recent years， the integration of UAV systems with deep learning algorithms has emerged as a prominent research focus. To demonstrate the advancement of UAV search and rescue techniques， the review of these methodologies has been conducted. Firstly， the difficulties faced by UAV search and rescue are analyzed according to the characteristics of UAV search and rescue missions. Subsequently， various UAV search and rescue methods based on deep learning in recent years are sorted out. Additionally， object detection datasets relevant to UAV search and rescue missions have been sorted out according to different data types. Finally， the problems of current research and future development directions are summarized.

Key words: UAV search and rescue, deep learning, object detection, detection technique, imaging environment interference

CLC Number:

V279

Bo PENG, Jikang BAI, Weiwen CHEN, Xiangtao ZHENG, Jianjun LEI, Xiaoqiang LU. Research progress for UAV search and rescue methods based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(23): 632761.

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Scale， collection scenarios and labeling of different datasets

数据类型	数据集	数据规模	采集场景	标注
可见光	HERIDAL	500张标注的全尺寸航拍图像	山地、荒野、森林等	人物位置边界框
	TinyPerson	1 610张标注图像	海洋、海滩等	目标类别标注及边界框
	SARD	1 981张标注图像	岩石区、森林等	人物位置边界框
	AFO	3 647张标注图像	海洋	目标类别标注及边界框
	Manipal-UAV	13 462张标注图像	学校、道路及建筑等	人物位置边界框
	Archangel	4 643 900张标注图像	草地、沙漠等	目标类别标注、边界框、人物姿态标注及无人机与目标相对位置的元数据
	C2A	10 215张标注图像	火灾、洪水、废墟及交通事故等	人物位置边界框、人物姿态及灾难场景
红外	BIRDSAI	172段标注红外视频	草地、水域及森林等	目标类别标注及边界框
	UAV thermal image	6 447张标注的红外图像	海滩、树木及建筑等	人物位置边界框、人物
	HIT-UAV	2 898张标注的红外图像	学校、道路及操场等	目标类别标注、边界框及无人机飞行高度、拍摄视角、时间及天气
	POP	8 768张标注的红外图像	树林、草地及山区等	人物位置边界框
雷达	UWB radar	270个完整采集会话	不同风力的野外场景	人体是否存在
多光谱	SeaDronesSee	超54 000张标注图像，包括432张多光谱图像	海洋	目标类别标注、边界框及无人机状态的元数据
	NII-CU	5 880对标注RGB和红外图像	棒球场、森林等	人物位置边界框
	WiSARD	55 942张标注图像，包括15 453对标注RGB和红外图像	森林、岩石区、海岸、山地及雪地等	人物位置边界框及包含时间、相机视角等的元数据
	RGBTDrone-Person	6 125对标注RGB和红外图像	森林、建筑及田野等	目标类别标注及边界框
	VTSaR	32 400张标注图像，包括9 602对标注RGB和红外图像	街区、海岸线、海洋、工业区及荒野等	人物位置边界框

Table 4

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Research progress for UAV search and rescue methods based on deep learning

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数据类型	数据集	发布年份	下载地址
可见光	HERIDAL	2019	https：∥universe.roboflow.com/licenta-ynwvo/heridal-lrbkc
	TinyPerson	2020	https：∥github.com/ucas-vg/TinyBenchmark
	SARD	2021	https：∥universe.roboflow.com/datasets-pdabr/sard-8xjhy
	AFO	2021	https：∥datasetninja.com/afo
	Manipal-UAV	2023	https：∥github.com/Akshathakrbhat/Manipal-UAV-Person-Dataset
	Archangel	2023	https：∥a2i2-archangel.vision
	C2A	2024	https：∥github.com/Ragib-Amin-Nihal/C2A
红外	BIRDSAI	2020	https：∥lila.science/datasets/conservationdrones
	UAV thermal image	2020	https：∥zenodo.org/records/4327118
	HIT-UAV	2023	https：∥www.kaggle.com/datasets/pandrii000/hituav-a-highaltitude-infrared-thermal-dataset
	POP	2025	https：∥osf.io/kmcva/
雷达	UWB radar dataset	2024	https：∥zenodo.org/records/10731867
多光谱	SeaDronesSee	2022	https：∥seadronessee.cs.uni-tuebingen.de./
	NII-CU	2022	https：∥www.nii-cu-multispectral.org/
	WiSARD	2022	https：∥sites.google.com/uw.edu/wisard/
	RGBTDronePerson	2023	https：∥nnnnerd.github.io/RGBTDronePerson/
	VTSaR	2025	https：∥github.com/zxq309/VTSaR

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