面向无人机航拍图像的目标检测研究综述

doi:10.11996/JG.j.2095-302X.2024061145

图学学报 ›› 2024, Vol. 45 ›› Issue (6): 1145-1164.DOI: 10.11996/JG.j.2095-302X.2024061145

• “大模型与图学技术及应用”专题 • 上一篇下一篇

面向无人机航拍图像的目标检测研究综述

李琼¹(), 考月英¹(), 张莹¹, 徐沛²

1.北京市科学技术研究院信息与人工智能技术研究所，北京 100089
2.中国科学院自动化研究所智能系统与工程研究中心，北京 100190

收稿日期:2024-08-02 接受日期:2024-09-22 出版日期:2024-12-31 发布日期:2024-12-24
通讯作者:考月英(1989-)，女，高级工程师，博士。主要研究方向为模式识别与机器视觉等。E-mail：kaoyueying@bjast.ac.cn
第一作者:李琼(1997-)，女，助理研究员，硕士。主要研究方向为目标检测与识别。E-mail：liqiong@bjast.ac.cn
基金资助:
国家自然科学基金(62406030);北京市科学技术研究院财政资助项目(24CE-BGS-01);北京市科学技术研究院财政资助项目(24CA004-03);北京市科学技术研究院财政资助项目(24CB012-01);国家资助博士后研究人员计划(GZC20232995)

Review on object detection in UAV aerial images

LI Qiong¹(), KAO Yueying¹(), ZHANG Ying¹, XU Pei²

1. Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing 100089, China
2. Center for Research on Intelligent System and Engineering, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received:2024-08-02 Accepted:2024-09-22 Published:2024-12-31 Online:2024-12-24
Contact: KAO Yueying (1989-), senior engineer, Ph.D. Her main research interests cover pattern recognition and machine vision, etc. E-mail：kaoyueying@bjast.ac.cn
First author：LI Qiong (1997-), assistant researcher, master. Her main research interests cover object detection and recognition. E-mail：liqiong@bjast.ac.cn
Supported by:
National Natural Science Foundation of China(62406030);Financial Program of BJAST(24CE-BGS-01);Financial Program of BJAST(24CA004-03);Financial Program of BJAST(24CB012-01);Postdoctoral Fellowship Program of CPSF(GZC20232995)

摘要/Abstract

摘要：

随着无人机和计算机视觉技术的快速发展与深度融合，面向无人机航拍图像的目标检测研究受到越来越多的关注，已广泛应用于精准农业、动物监测、城市管理、应急救援等领域。与普通视角下拍摄的图像相比，无人机航拍图像具有视野更广、目标尺寸显著缩小、视角和尺度灵活多变等特点，无法完全适用普通视角下的目标检测方法。基于此，首先详细回顾了普通视角下目标检测方法的研究进展，包括传统方法、深度学习方法和基于大模型的方法，随后综述了现有目标检测方法针对无人机航拍图像目标检测中的图像质量下降、尺度和视角变化、小目标检测难度大、复杂背景及遮挡、大视场中的不均衡，以及实时性要求高等6大难点问题提出的创新策略和优化方法。此外，归纳总结了无人机航拍图像目标检测数据集，并在2个具有代表性的数据集上对现有方法进行性能分析。最后，根据无人机航拍图像目标检测领域仍存在的问题，展望了未来可能的研究方向，为无人机航拍图像目标检测的发展和应用提供参考。

关键词: 无人机航拍图像, 深度学习, 计算机视觉, 目标检测, 多尺度目标

Abstract:

With the rapid development and deep integration of unmanned aerial vehicle (UAV) and computer vision technologies, research on object detection in UAV aerial images has gained increasing attention and has been widely applied in precision agriculture, animal monitoring, urban management, emergency rescue, and other fields. Compared to images captured from conventional perspectives, images acquired by UAVs feature a wider field of view, significantly reduced object size, and variations in viewpoint and scale, rendering conventional object detection methods inadequate. Accordingly, a detailed review of progress in object detection methods from a conventional perspective was first provided, including traditional methods, deep learning methods, and large-model-based methods. Subsequently, the innovative strategies and optimization methods proposed by existing object detection methods were summarized, specifically addressing six challenging issues specific to UAV aerial image object detection, i.e., image quality degradation, scale and viewpoint variation, small-object detection difficulty, complex background and occlusion, imbalance in large fields of view, and high real-time requirements. Additionally, UAV aerial image object detection datasets were consolidated and analyzed, with an evaluation of the performance of existing methods on two representative datasets. Finally, potential research directions for the future were outlined based on the unresolved issues in the field of UAV aerial image object detection, providing reference for the development and application of object detection in UAV aerial images.

Key words: unmanned aerial vehicle aerial image, deep learning, computer vision, object detection, multi-scale objects

中图分类号:

TP391

李琼, 考月英, 张莹, 徐沛. 面向无人机航拍图像的目标检测研究综述[J]. 图学学报, 2024, 45(6): 1145-1164.

LI Qiong, KAO Yueying, ZHANG Ying, XU Pei. Review on object detection in UAV aerial images[J]. Journal of Graphics, 2024, 45(6): 1145-1164.

图/表 6

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数据集	图像数量/ 张	标注数量/ 个	飞行高度/m	分辨率	目标类别	任务	发布时间/ 年	下载链接
CARPK^[148]	1 448	89 777	40	1280×720	汽车	车辆计数	2017	https://lafi.github.io/LPN/
UAVDT^[149]	80 000	840 000	10~30、 31~70、 >70	1080×540	汽车、卡车和公共汽车	车辆检测和追踪	2018	https://sites.google.com/site/daviddo0323/
VisDrone^[20]	8 599	540 000	-	2000×1500	行人、人、汽车、面包车、公共汽车、卡车等10个类别	目标检测	2018	https://github.com/VisDrone/VisDrone-Dataset
DAC-SDC^[153]	150 000	-	-	640×360	人、汽车、船、建筑等12个大类及95个子类	目标检测	2019	https://github.com/xyzxinyizhang/2018-DAC-System-Design-Contest
AU-Air^[154]	32 823	132 000	5~30	1920×1080	人、汽车、货车、卡车、摩托车等 8个类别	交通监视	2020	https://bozcani.github.io/auairdataset
UVSD^[116]	5 874	98 600	10~150	960×540~ 5280×2970	车辆	车辆检测和分割	2020	https://github.com/liuchunsense/UVSD
MOHR^[155]	10 631	90 014	200、300 和400	5472×3078； 7360×4192； 8688×5792	建筑、汽车、卡车、塌陷、洪灾等5个类别	多尺度目标检测	2021	/
DroneVehicle^[156]	56 878	819 000	-	840×712	汽车、公共汽车、面包车、货车和卡车等5个类别	车辆检测	2022	https://github.com/VisDrone/DroneVehicle
Manipal-UAV person detection dataset^[157]	13 462	153 112	10~50	1280×720	人	小目标人员检测	2022	https://github.com/Akshathakrbhat/Manipal-UAV-Person-Dataset
SeaDroneSee^[158]	54 000	400 000	5~260	3840×2160~ 5456×3632	船、救生衣、游泳者等6个类别	海上人员检测	2022	https://seadronessee.cs.uni-tuebingen.de
RTDOD^[159]	32 400	179 672	-	1280×720	人、狗、自行车、运动球、摩托车、船等10个类别	目标检测	2023	https://github.com/fenght96/RTDOD
WAID^[160]	14 375	-	-	1018×572	羊、牛、海豹等 6个类别	野生动物监测	2023	https://github.com/xiaohuicui/WAID
NVD^[161]	8 450	26 313	120~250	1920×1080~ 3840×2160	车辆	车辆检测	2023	https://nvd.ltu-ai.dev/
UEMM-Air^[162]	20 000	-	5~50	1920×1080	城市、公园等 13大类场景，上百种车型	车辆检测	2024	https://github.com/1e12Leon/UEMM-Air

数据集	图像数量/ 张	标注数量/ 个	飞行高度/m	分辨率	目标类别	任务	发布时间/ 年	下载链接
CARPK^[148]	1 448	89 777	40	1280×720	汽车	车辆计数	2017	https://lafi.github.io/LPN/
UAVDT^[149]	80 000	840 000	10~30、 31~70、 >70	1080×540	汽车、卡车和公共汽车	车辆检测和追踪	2018	https://sites.google.com/site/daviddo0323/
VisDrone^[20]	8 599	540 000	-	2000×1500	行人、人、汽车、面包车、公共汽车、卡车等10个类别	目标检测	2018	https://github.com/VisDrone/VisDrone-Dataset
DAC-SDC^[153]	150 000	-	-	640×360	人、汽车、船、建筑等12个大类及95个子类	目标检测	2019	https://github.com/xyzxinyizhang/2018-DAC-System-Design-Contest
AU-Air^[154]	32 823	132 000	5~30	1920×1080	人、汽车、货车、卡车、摩托车等 8个类别	交通监视	2020	https://bozcani.github.io/auairdataset
UVSD^[116]	5 874	98 600	10~150	960×540~ 5280×2970	车辆	车辆检测和分割	2020	https://github.com/liuchunsense/UVSD
MOHR^[155]	10 631	90 014	200、300 和400	5472×3078； 7360×4192； 8688×5792	建筑、汽车、卡车、塌陷、洪灾等5个类别	多尺度目标检测	2021	/
DroneVehicle^[156]	56 878	819 000	-	840×712	汽车、公共汽车、面包车、货车和卡车等5个类别	车辆检测	2022	https://github.com/VisDrone/DroneVehicle
Manipal-UAV person detection dataset^[157]	13 462	153 112	10~50	1280×720	人	小目标人员检测	2022	https://github.com/Akshathakrbhat/Manipal-UAV-Person-Dataset
SeaDroneSee^[158]	54 000	400 000	5~260	3840×2160~ 5456×3632	船、救生衣、游泳者等6个类别	海上人员检测	2022	https://seadronessee.cs.uni-tuebingen.de
RTDOD^[159]	32 400	179 672	-	1280×720	人、狗、自行车、运动球、摩托车、船等10个类别	目标检测	2023	https://github.com/fenght96/RTDOD
WAID^[160]	14 375	-	-	1018×572	羊、牛、海豹等 6个类别	野生动物监测	2023	https://github.com/xiaohuicui/WAID
NVD^[161]	8 450	26 313	120~250	1920×1080~ 3840×2160	车辆	车辆检测	2023	https://nvd.ltu-ai.dev/
UEMM-Air^[162]	20 000	-	5~50	1920×1080	城市、公园等 13大类场景，上百种车型	车辆检测	2024	https://github.com/1e12Leon/UEMM-Air

对应问题	方法	基础网络	训练/测试	输入尺寸/像素	AP	AP₅₀	AP₇₅	发表时间/年
图像质量下降	TRẦN等^[90]	FFA-Net+PAA	23 384/2 181	-	12.50	-	-	2022
尺度和视角变化	DSYOLOv3^[101]	YOLOv3	24 143 /16 592	608×608	9.80	23.40	5.00	2021
尺度和视角变化	DFPN^[103]	-	23 258/15 069	640×640	17.10	29.30	18.10	2023
小目标检测	DNOD^[111]	YOLOv4	23 258/15 069	1080×540	14.20	31.90	11.00	2021
小目标检测	DNOD^[111]	EfficientDet-D7	23 258/15 069	1080×540	12.90	32.00	10.90	2021
复杂背景及遮挡	FiFoNet^[119]	-	23 258/15 069	最长边1536	21.30	36.80	22.50	2022
样本不均衡	ClusDet^[129]	ResNet50	23 258/15 069	600×1000	13.70	26.50	12.50	2019
	DMNet^[120]	ResNet 50	23 258/15 069	600×1000	14.70	24.60	16.30	2020
	BSSD^[121]	ResNet 101	23 829/16 580	640×640	19.27	30.71	19.96	2021
	BSSD^[121]	ResNet 101	23 829/16 580	640×640	18.14	29.37	19.83	2021
	DSHNet^[126]	ResNet-50	23 258/15 069	600×1000	17.80	30.40	19.70	2021
	GLSAN^[131]	ResNet-50	23 258/15 069	600×1000	19.00	30.50	21.70	2021
	UCGNet^[132]	Yolov5	23 258/15 069	1080×540	19.10	36.70	18.00	2021
	PRDet^[124]	ResNet-50	23 258/15 069	600×1000	19.80	34.10	21.30	2023
	LI等^[133]	DetNet-59	23 258/15 069	1080×540	15.30	29.30	16.20	2023
综合改进	NDFT^[142]	ResNet101	23 258/15 069	1080×540	52.03	-	-	2019
	PENet^[143]	-	23 258/15 069	-	67.30	76.30	74.60	2020
	UFPMP-Det^[145]	ResNet-50	23 258/15 069	600×1000	24.60	38.70	28.00	2022

对应问题	方法	基础网络	训练/测试	输入尺寸/像素	AP	AP₅₀	AP₇₅	发表时间/年
图像质量下降	TRẦN等^[90]	FFA-Net+PAA	23 384/2 181	-	12.50	-	-	2022
尺度和视角变化	DSYOLOv3^[101]	YOLOv3	24 143 /16 592	608×608	9.80	23.40	5.00	2021
尺度和视角变化	DFPN^[103]	-	23 258/15 069	640×640	17.10	29.30	18.10	2023
小目标检测	DNOD^[111]	YOLOv4	23 258/15 069	1080×540	14.20	31.90	11.00	2021
小目标检测	DNOD^[111]	EfficientDet-D7	23 258/15 069	1080×540	12.90	32.00	10.90	2021
复杂背景及遮挡	FiFoNet^[119]	-	23 258/15 069	最长边1536	21.30	36.80	22.50	2022
样本不均衡	ClusDet^[129]	ResNet50	23 258/15 069	600×1000	13.70	26.50	12.50	2019
	DMNet^[120]	ResNet 50	23 258/15 069	600×1000	14.70	24.60	16.30	2020
	BSSD^[121]	ResNet 101	23 829/16 580	640×640	19.27	30.71	19.96	2021
	BSSD^[121]	ResNet 101	23 829/16 580	640×640	18.14	29.37	19.83	2021
	DSHNet^[126]	ResNet-50	23 258/15 069	600×1000	17.80	30.40	19.70	2021
	GLSAN^[131]	ResNet-50	23 258/15 069	600×1000	19.00	30.50	21.70	2021
	UCGNet^[132]	Yolov5	23 258/15 069	1080×540	19.10	36.70	18.00	2021
	PRDet^[124]	ResNet-50	23 258/15 069	600×1000	19.80	34.10	21.30	2023
	LI等^[133]	DetNet-59	23 258/15 069	1080×540	15.30	29.30	16.20	2023
综合改进	NDFT^[142]	ResNet101	23 258/15 069	1080×540	52.03	-	-	2019
	PENet^[143]	-	23 258/15 069	-	67.30	76.30	74.60	2020
	UFPMP-Det^[145]	ResNet-50	23 258/15 069	600×1000	24.60	38.70	28.00	2022

对应问题	方法	基础网络	训练/测试	AP	AP₅₀	AP₇₅	AR₁	AR₁₀	AR₁₀₀	AR₅₀₀	发表时间/年
图像质量下降	DCNet^[88]	CenterNet	6 471/1 610	29.43	-	-	-	-	-	-	2021
尺度和视角变化	RRNet^[98]	Hourglass	6 741/1580	29.13	55.82	27.23	1.02	8.50	35.19	46.05	2019
	SAMFR^[99]	DetNet-59	6 471/548	33.72	58.62	33.88	0.53	3.40	22.60	46.03	2019
	SAMFR^[99]	DetNet-59	6 471/1 580	20.18	40.03	18.42	0.46	3.49	21.60	30.82	2019
	ECascade-RCNN^[100]	Trident-FPN	6 371/521	28.40	-	-	-	-	-	-	2021
	DSYOLOv3^[101]	YOLOv3	6 471/548	22.30	44.50	20.30	-	-	-	-	2021
	SPB-YOLO^[102]	YOLOv5	6 471/1 580	40.10	-	-	-	-	-	-	2021
	DFPN^[103]	-	6 471/548	30.30	51.90	30.50	-	-	-	-	2023
小目标检测	Zhang等^[107]	ResNet50+RPN	6 471/1 580	22.61	45.16	19.94	0.42	2.84	17.10	35.27	2019
	MPFPN^[108]	ResNet-101	6 471/1 580	29.05	54.38	26.99	0.55	5.81	35.57	45.69	2020
	Jadhav等^[109]	ResNet-50	6 471/1 580	11.19	25.65	8.78	0.56	4.87	17.19	24.09	2020
	HRDNet^[110]	ResNeXt50+101	3 564/1 725	35.51	62.00	35.13	-	-	-	-	2021
	DNOD^[111]	YOLOv4	6 471/1 610	54.88	-	-	-	-	-	-	2021
	DNOD^[111]	EfficientDet-D7	6 471/1 610	53.76	-	-	-	-	-	-	2021
	Shang等^[112]	YOLOv5s	6.471/1 610	36.40	-	-	-	-	-	-	2023
	Zhao等^[113]	YOLOv7	6 471/548	56.80	-	-	-	-	-	-	2023
	YOLOv7-UAV^[114]	YOLOv7	-	45.30	-	-	-	-	-	-	2024
复杂背景及遮挡	D-A-FS SSD^[117]	VGG16	6 471/548	36.70	-	-	-	-	-	-	2020
复杂背景及遮挡	FiFoNet^[119]	-	6 471/548	36.91	63.80	36.11	-	-	-	-	2022
样本不均衡	ClusDet^[129]	ResNeXt101	6 471/548	32.40	56.20	31.60	-	-	-	-	2019
	Hong等^[125]	ResNet-101	6.471/1 610	29.13	54.70	27.38	0.32	1.48	9.46	44.53	2019
	Hong等^[125]	ResNet-101	6 471/548	37.15	65.54	36.56	0.32	1.47	7.28	53.78	2019
	CRENet^[130]	Hourglass-104	6 471/548	33.70	54.30	33.50	-	-	-	-	2020
	DMNet^[120]	ResNeXt 101	6 471/548	29.40	49.30	30.60	-	-	-	-	2020
	DSHNet^[126]	ResNet-50	6 471/548	30.30	51.80	30.90	-	-	-	-	2021
	GLSAN^[131]	ResNet-50	6 471/548	32.50	55.80	33.00	-	-	-	-	2021
	UCGNet^[132]	Yolov5	6 471/548	32.80	53.10	33.90	-	-	-	-	2021
	VAMYOLOX^[123]	Darknet53	6 471/548	29.40	47.00	-	-	-	-	-	2023
	PRDet^[124]	ResNeXt-101	6 471/548	40.20	62.00	43.50	-	-	-	-	2023
	Li等^[133]	DetNet59	6 471/548	31.40	54.50	27.30	-	-	-	-	2023
实时检测	SlimYOLOv3^[135]	YOLOv3	6 471/548	23.90	-	-	-	-	-	-	2019
	LAI-YOLOv5s^[140]	YOLOv5	6 471/548	-	40.40	-	-	-	-	-	2023
	Cao等^[141]	YOLOv5	6 471/549	27.70	46.90	-	-	-	-	-	2023
综合改进	NDFT^[142]	ResNet101	6 471/548	52.77			-	-	-	-	2019
	PENet^[143]	-	6 471/548	41.10	58.00	44.30	-	-	-	-	2020
	SyNet^[144]	CenterNet	6 471/1580	25.10	48.40	26.20	-	-	-	-	2021
	UFPMP-Det^[145]	ResNeXt-101	6 471/548	40.10	66.80	41.30	-	-	-	-	2022
	YOLO-UAV^[146]	YOLOv5l	6 471/548	30.50			-	-	-	-	2022
	YOLOv7X+^[147]	YOLOV7	6 471/548	-	60.30	-	-	-	-	-	2023

面向无人机航拍图像的目标检测研究综述

Review on object detection in UAV aerial images

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