Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning

WANG Zhong-tian; WU Yi-quan

doi:10.12263/DZXB.20250775

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Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning

SURVEYS AND REVIEWS | 更新时间：2026-02-10

- Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning
- ACTA ELECTRONICA SINICA Vol. 53, Issue 11, Pages: 4171-4198(2025)
- 作者机构：
  
  南京航空航天大学电子信息工程学院，江苏南京 211106
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61573183)
- DOI：10.12263/DZXB.20250775
  CLC： TP391.41;V279
- Received：05 September 2025，
  
  Accepted：18 November 2025，
  
  Published：25 November 2025
- 稿件说明：
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王中天, 吴一全. 基于视觉与深度学习的无人机自主着陆场景感知方法研究进展[J]. 电子学报, 2025, 53(11): 4171-4198.

WANG Zhong-tian, WU Yi-quan. Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning[J]. Acta Electronica Sinica, 2025, 53(11): 4171-4198.
王中天, 吴一全. 基于视觉与深度学习的无人机自主着陆场景感知方法研究进展[J]. 电子学报, 2025, 53(11): 4171-4198. DOI：10.12263/DZXB.20250775

WANG Zhong-tian, WU Yi-quan. Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning[J]. Acta Electronica Sinica, 2025, 53(11): 4171-4198. DOI：10.12263/DZXB.20250775

摘要

随着无人机（Unmanned Aerial Vehicle，UAV）技术的蓬勃发展，其在军事国防、智能交通、设施巡检、灾害救援、农业管理等众多领域的应用日益广泛，成为低空经济发展的核心驱动力.自主着陆作为无人机关键核心技术之一，直接决定了无人机作业的安全性与可靠性，尤其在电池电量不足、气象条件恶化或通信中断等紧急场景下，能有效避免设备损坏与事故发生，是实现无人机完全自动化的关键环节.基于视觉与深度学习的场景感知技术，凭借强大的特征学习和模式识别能力，突破了传统依赖GPS（Global Positioning System）、激光雷达（Light Detection And Ranging，LiDAR）等技术在复杂环境中的局限性，为无人机自主着陆领域带来了全新的解决方案.本文系统综述了基于视觉与深度学习的无人机自主着陆场景感知方法.首先阐述了深度学习在无人机自主着陆中的应用背景和重要性，梳理了从传统传感器驱动到智能感知的技术演进历程.随后详细剖析了不同场景的特征与技术挑战：静态平台着陆聚焦降落标识、跑道检测、地基引导三类场景，核心需求是提升着陆精度与准确率；动态平台着陆涵盖车载陆地、舰艇海上及其他移动平台，需重点解决运动跟踪与干扰抑制问题；特殊场景着陆则面临山区、森林、城市峡谷等复杂环境中的障碍物遮挡、信号干扰、极端气象等多重挑战.本文深入探讨了核心技术体系，包括目标检测、语义分割、姿态估计、光流预测、三维重建等关键技术的原理与应用.同时分析了特征提取优化、语义理解增强及场景适配策略的应用效果与性能表现.最后总结了该领域面临的复杂环境适应性不足、计算资源约束、数据依赖与标注难题等挑战，并对未来研究方向进行了展望.指出通过多源传感器数据融合可提升复杂环境感知能力，开发轻量化模型能适配无人机资源限制，加强仿真与真实场景结合可提高模型泛化能力.本文通过系统地总结与分析，全面呈现了该领域的技术现状与发展脉络，为无人机自主着陆技术的进一步研究与工程应用提供了宝贵的参考和指导.

Abstract

With the vigorous development of unmanned aerial vehicle (UAV) technology

its applications in various fields such as military defense

intelligent transportation

facility inspection

disaster relief

and agricultural management have become increasingly widespread

becoming the core driving force for the development of the low-altitude economy. Autonomous landing

as one of the core and key technologies of UAVs

directly determines the safety and reliability of UAV operations. Especially in emergency scenarios such as low battery power

deteriorating weather conditions

or communication disruptions

it can effectively prevent equipment damage and accidents

and is a crucial step towards achieving full automation of UAVs. Scene perception technology based on vision and deep learning

with its powerful feature learning and pattern recognition capabilities

has broken through the limitations of traditional technologies such as GPS (Global Positioning System) and LiDAR (Light Detection And Ranging) in complex environments

bringing a brand-new solution to the field of UAV autonomous landing. This paper systematically reviews the scene perception methods for UAV autonomous landing based on vision and deep learning. Firstly

it elaborates on the application background and significance of deep learning in UAV autonomous landing

and sorts out the technological evolution from traditional sensor-driven to intelligent perception. Then

it analyzes in detail the features and technical challenges of different scenarios: static platform landing focuses on three types of scenarios - landing marks

runway detection

and ground guidance

with the core demand being to improve landing accuracy and reliability; dynamic platform landing covers land-based vehicles

ships at sea

and other mobile platforms

and needs to focus on solving problems of motion tracking and interference suppression; special scenario landing faces multiple challenges such as obstacle occlusion

signal interference

and extreme weather in complex environments like mountains

forests

and urban canyons. This paper deeply explores the core technical system

including the principles and applications of key technologies such as object detection

semantic segmentation

pose estimation

optical flow prediction

and 3D reconstruction. At the same time

it analyzes the application effects and performance of feature extraction optimization

semantic understanding enhancement

and scene adaptation strategies. Finally

it summarizes the challenges faced in this field

such as insufficient adaptability to complex environments

computational resource constraints

data dependence and annotation difficulties

and looks forward to future research directions. It points out that multi-source sensor data fusion can enhance the perception ability in complex environments

developing lightweight models can adapt to the resource limitations of UAVs

and strengthening the combination of simulation and real scenarios can improve the generalization ability of models. Through systematic summary and analysis

this paper comprehensively presents the current technical status and development trends in this field

providing valuable reference and guidance for further research and engineering applications of UAV autonomous landing technology.

关键词

Keywords

references

SHAH ALAM M , OLUOCH J . A survey of safe landing zone detection techniques for autonomous unmanned aerial vehicles (UAVs) [J ] . Expert Systems with Applications , 2021 , 179 : 115091 .

MANGINA E , O’KEEFFE E , EYERMAN J , et al . Drones for live streaming of visuals for people with limited mobility [C ] // 2016 22nd International Conference on Virtual System & Multimedia . Piscataway : IEEE , 2017 : 1 - 6 .

GAUTAM A , SUJIT P B , SARIPALLI S . A survey of autonomous landing techniques for UAVs [C ] // 2014 International Conference on Unmanned Aircraft Systems . Piscataway : IEEE , 2014 : 1210 - 1218 .

钟映春 , 张文祥 , 王波 , 等 . 电力巡检无人机自主降落的引导系统与策略 [J ] . 光学精密工程 , 2022 , 30 ( 11 ): 1362 - 1373 .

ZHONG Y C , ZHANG W X , WANG B , et al . Navigation system and strategies for electric inspecting UAV autonomously landing [J ] . Optics and Precision Engineering , 2022 , 30 ( 11 ): 1362 - 1373 . (in Chinese)

RAEI H , CHO Y , PARK K . Autonomous landing on moving targets using LiDAR, Camera and IMU sensor Fusion [C ] // 2022 13th Asian Control Conference . Piscataway : IEEE , 2022 : 419 - 423 .

CHO G , CHOI J , BAE G , et al . Autonomous ship deck landing of a quadrotor UAV using feed-forward image-based visual servoing [J ] . Aerospace Science and Technology , 2022 , 130 : 107869 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [J ] . Communications of the ACM , 2017 , 60 ( 6 ): 84 - 90 .

ZAREMBA W , SUTSKEVER I , VINYALS O . Recurrent neural network regularization [EB/OL ] . ( 2015-02-19 )[ 2025-10-10 ] . https://arXiv.org/abs/1409.2329 https://arXiv.org/abs/1409.2329 .

WANG Y X , ZHU J G , CAO L , et al . Long short-term memory network with transfer learning for lithium-ion battery capacity fade and cycle life prediction [J ] . Applied Energy , 2023 , 350 : 121660 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [EB/OL ] . ( 2023-08-02 )[ 2025-10-10 ] . https://arxiv.org/abs/1706.03762 https://arxiv.org/abs/1706.03762 .

谷美颖 , 李航 , 张家伟 , 等 . 基于视觉的无人机定位与导航方法研究综述 [J ] . 电子学报 , 2025 , 53 ( 3 ): 651 - 685 .

GU M Y , LI H , ZHANG J W , et al . A review of vision-based UAV localization and navigation methods [J ] . Acta Electronica Sinica , 2025 , 53 ( 3 ): 651 - 685 . (in Chinese)

钟春来 , 杨洋 , 曹立佳 , 等 . 基于视觉的无人机自主着陆研究综述 [J ] . 航空兵器 , 2023 , 30 ( 5 ): 104 - 114 .

ZHONG C L , YANG Y , CAO L J , et al . A review of vision-based autonomous UAV landing research [J ] . Aero Weaponry , 2023 , 30 ( 5 ): 104 - 114 . (in Chinese)

KATKURI A V R , MADAN H , KHATRI N , et al . Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review [J ] . Array , 2024 , 23 : 100361 .

马宁 , 曹云峰 . 面向无人机自主着陆的视觉感知与位姿估计方法综述 [J ] . 自动化学报 , 2024 , 50 ( 7 ): 1284 - 1304 .

MA N , CAO Y F . A survey on vision-based sensing and pose estimation methods for UAV autonomous landing [J ] . Acta Automatica Sinica , 2024 , 50 ( 7 ): 1284 - 1304 . (in Chinese)

HASSANALIAN M , ABDELKEFI A . Classifications, applications, and design challenges of drones: A review [J ] . Progress in Aerospace Sciences , 2017 , 91 : 99 - 131 .

赵良玉 , 李丹 , 赵辰悦 , 等 . 无人机自主降落标识检测方法若干研究进展 [J ] . 航空学报 , 2022 , 43 ( 9 ): 025882 .

ZHAO L Y , LI D , ZHAO C Y , et al . Some achievements on detection methods of UAV autonomous landing markers [J ] . Acta Aeronautica et Astronautica Sinica , 2022 , 43 ( 9 ): 025882 . (in Chinese)

XIN L , TANG Z M , GAI W Q , et al . Vision-based autonomous landing for the UAV: A review [J ] . Aerospace , 2022 , 9 ( 11 ): 634 .

CHEN C L , ZHENG Z Y , XU T Y , et al . YOLO-based UAV technology: A review of the research and its applications [J ] . Drones , 2023 , 7 ( 3 ): 190 .

GARCÍA-PULIDO J A , PAJARES G , DORMIDO S , et al . Recognition of a landing platform for unmanned aerial vehicles by using computer vision-based techniques [J ] . Expert Systems with Applications , 2017 , 76 : 152 - 165 .

MORE D S , SURESH S , D’SOUZA J M , et al . Landmark detection for auto landing of quadcopter using YOLOv5 [C ] // Intelligent Control, Robotics, and Industrial Automation . Singapore : Springer , 2023 : 3 - 12 .

OLSON E . AprilTag: A robust and flexible visual fiducial system [C ] // 2011 IEEE International Conference on Robotics and Automation . Piscataway : IEEE , 2011 : 3400 - 3407 .

WANG J , OLSON E . AprilTag 2: Efficient and robust fiducial detection [C ] // 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems . Piscataway : IEEE , 2016 : 4193 - 4198 .

MU L X , LI Q L , WANG B , et al . A vision-based autonomous landing guidance strategy for a micro-UAV by the modified camera view [J ] . Drones , 2023 , 7 ( 6 ): 400 .

WU J G , ZHANG Z , HUANG W H . Semantic map construction of UAV autonomous landing in unknown environment [C ] // 2024 36th Chinese Control and Decision Conference . Piscataway : IEEE , 2024 : 5018 - 5025 .

WANG Z , WANG B , TANG C Y , et al . Pose and velocity estimation algorithm for UAV in visual landing [C ] // 2020 39th Chinese Control Conference . Piscataway : IEEE , 2020 : 3713 - 3718 .

乐辉 . 基于视觉的四旋翼无人机着陆标识分类与识别研究 [D ] . 镇江 : 江苏大学 , 2021 .

LE H . Research on Classification and Recognition of Quad-rotor UAV Landing Sign based on Vision [D ] . Zhenjiang : Jiangsu University , 2021 . (in Chinese)

方琪鸿 . 基于单目视觉与惯性融合的无人机自主降落导航技术研究 [D ] . 成都 : 电子科技大学 , 2024 .

FANG Q H . Research on Autonomous Landing Navigation Technology for Unmanned Aerial Vehicles Based on Monocular Vision and Inertial Fusion [D ] . Chengdu : University of Electronic Science and Technology of China , 2024 . (in Chinese)

MA M Y , SHEN S G , HUANG Y C . Enhancing UAV visual landing recognition with YOLO’s object detection by onboard edge computing [J ] . Sensors , 2023 , 23 ( 21 ): 8999 .

MA N , WENG X R , CAO Y F , et al . Monocular-vision-based precise runway detection applied to state estimation for carrier-based UAV landing [J ] . Sensors , 2022 , 22 ( 21 ): 8385 .

LIU X X , XUE W H , XU X L , et al . Research on unmanned aerial vehicle (UAV) visual landing guidance and positioning algorithms [J ] . Drones , 2024 , 8 ( 6 ): 257 .

LI Y , XIA Y , ZHENG G J , et al . YOLO-RWY: A novel runway detection model for vision-based autonomous landing of fixed-wing unmanned aerial vehicles [J ] . Drones , 2024 , 8 ( 10 ): 571 .

WANG Z Y , ZHAO D , CAO Y F . Visual navigation algorithm for night landing of fixed-wing unmanned aerial vehicle [J ] . Aerospace , 2022 , 9 ( 10 ): 615 .

WANG Q , FENG W Q , ZHAO H B , et al . VALNet: Vision-based autonomous landing with airport runway instance segmentation [J ] . Remote Sensing , 2024 , 16 ( 12 ): 2161 .

马宁 , 曹云峰 , 王指辉 , 等 . 基于YOLOv5网络架构的着陆跑道检测算法研究 [J ] . 激光与光电子学进展 , 2022 , 59 ( 14 ): 199 - 205 .

MA N , CAO Y F , WANG Z H , et al . Landing runway detection algorithm based on YOLOv5 network architecture [J ] . Laser & Optoelectronics Progress , 2022 , 59 ( 14 ): 199 - 205 . (in Chinese)

ZHANG X P , HE Z Z , MA Z , et al . VIAE-net: An end-to-end altitude estimation through monocular vision and inertial feature fusion neural networks for UAV autonomous landing [J ] . Sensors , 2021 , 21 ( 18 ): 6302 .

WUBBEN J , FABRA F , CALAFATE C T , et al . Accurate landing of unmanned aerial vehicles using ground pattern recognition [J ] . Electronics , 2019 , 8 ( 12 ): 1532 .

KHAN M U , DIL M , MISBAH M , et al . TransLearn-YOLOx: Improved-YOLO with transfer learning for fast and accurate multiclass UAV detection [C ] // 2023 International Conference on Communication, Computing and Digital Systems (C-CODE) . Piscataway : IEEE , 2023 : 1 - 7 .

XIE M J , CAO Y R , JIANG C H , et al . Object detection in UAV ground-based visual landing process based on improved faster R-CNN [M ] // Advances in Guidance, Navigation and Control . Singapore : Springer Nature Singapore , 2023 : 5012 - 5021 .

TANG D Q , SHEN L C , XIANG X J , et al . N-cameras-enabled joint pose estimation for auto-landing fixed-wing UAVs [J ] . Drones , 2023 , 7 ( 12 ): 693 .

ARAAR O , AOUF N , VITANOV I . Vision based autonomous landing of multirotor UAV on moving platform [J ] . Journal of Intelligent & Robotic Systems , 2017 , 85 ( 2 ): 369 - 384 .

TZOUMANIKAS D , LI W B , GRIMM M , et al . Fully autonomous micro air vehicle flight and landing on a moving target using visual-inertial estimation and model-predictive control [J ] . Journal of Field Robotics , 2019 , 36 ( 1 ): 49 - 77 .

蔡炳锋 . 基于机器视觉的无人机自适应移动着陆研究 [D ] . 杭州 : 浙江大学 , 2020 .

CAI B F . Research on Vision Based Adaptive Landing of UAV on a Moving Plattform [D ] . Hangzhou : Zhejiang University , 2020 . (in Chinese)

WANG Z L , SHE H P , SI W Y . Autonomous landing of multi-rotors UAV with monocular gimbaled camera on moving vehicle [C ] // 2017 13th IEEE International Conference on Control & Automation . Piscataway : IEEE , 2017 : 408 - 412 .

BACA T , STEPAN P , SPURNY V , et al . Autonomous landing on a moving vehicle with an unmanned aerial vehicle [J ] . Journal of Field Robotics , 2019 , 36 ( 5 ): 874 - 891 .

BOROWCZYK A , NGUYEN D T , NGUYEN A P , et al . Autonomous landing of a quadcopter on a high-speed ground vehicle [J ] . Journal of Guidance, Control, and Dynamics , 2017 , 40 ( 9 ): 2378 - 2385 .

JIANG S J , LUO B , LIU J , et al . UAV-based vehicle detection by multi-source images [C ] // the 2nd CCF Chinese Conference . Singapore : Springer , 2017 : 38 - 49 .

王忠言 , 李波 , 刘茹艳 , 等 . 基于yolov4算法的无人机单目测距算法 [J ] . 机械设计与制造工程 , 2022 , 51 ( 3 ): 58 - 62 .

WANG Z Y , LI B , LIU R Y , et al . UAV monocular ranging algorithm based on yolov4 algorithm [J ] . Machine Design and Manufacturing Engineering , 2022 , 51 ( 3 ): 58 - 62 . (in Chinese)

IDROVO P , VALLADOLID J D , DUTAN D , et al . A novel proposal for traffic officer detection in autonomous vehicles using convolutional networks YOLO v3, v5, and v8 [EB/OL ] . ( 2024-05-16 )[ 2025-10-10 ] . https://www.preprints.org/manuscript/202405.1078 https://www.preprints.org/manuscript/202405.1078 .

SHEN K , ZHUANG Y , CHEN Y X , et al . AeroNet: An efficient relative localization and object detection network for cooperative aerial-ground unmanned vehicles [J ] . Pattern Recognition Letters , 2023 , 171 : 28 - 37 .

LI J H , WANG X H , CUI H R , et al . Research on detection technology of autonomous landing based on airborne vision [J ] . IOP Conference Series: Earth and Environmental Science , 2020 , 440 ( 4 ): 042093 .

刘健 , 张祥甫 , 于志军 , 等 . 基于改进ERFNet的无人直升机着舰环境语义分割 [J ] . 电讯技术 , 2020 , 60 ( 1 ): 40 - 46 .

LIU J , ZHANG X F , YU Z J , et al . Semantic segmentation of landing environment for unmanned helicopter based on improved ERFNet [J ] . Telecommunication Engineering , 2020 , 60 ( 1 ): 40 - 46 . (in Chinese)

QIU J T , YU F F , XU F R , et al . Improved you only look once model for UAVs/ships relative attitude detection [M ] // Proceedings of 2024 Chinese Intelligent Systems Conference . Singapore : Springer Nature Singapore , 2024 : 218 - 226 .

ZHOU R , SHE J Y , QI N M , et al . Pose estimation algorithm for helicopter landing based on YOLO and PNP [C ] // Advances in Guidance, Navigation and Control . Singapore : Springer , 2022 : 3019 - 3028 .

XU G L , ZHANG Y , JI S Y , et al . Research on computer vision-based for UAV autonomous landing on a ship [J ] . Pattern Recognition Letters , 2009 , 30 ( 6 ): 600 - 605 .

POLVARA R , SHARMA S , WAN J , et al . Towards autonomous landing on a moving vessel through fiducial markers [C ] // 2017 European Conference on Mobile Robots . Piscataway : IEEE , 2017 : 1 - 6 .

任毅 . 基于视觉引导的旋翼无人机移动平台自主着陆技术研究 [D ] . 绵阳 : 西南科技大学 , 2019 .

REN Y . Research on Vision Based Autonomous Landing of Rotor UAV on a Moving Platform [D ] . Mianyang : Southwest University of Science and Technology , 2019 . (in Chinese)

WANG L , JIANG X Q , WANG D , et al . Research on aerial autonomous docking and landing technology of dual multi-rotor UAV [J ] . Sensors , 2022 , 22 ( 23 ): 9066 .

ARAFAT M Y , ALAM M M , MOH S . Vision-based navigation techniques for unmanned aerial vehicles: Review and challenges [J ] . Drones , 2023 , 7 ( 2 ): 89 .

SARIPALLI S , MONTGOMERY J F , SUKHATME G S . Vision-based autonomous landing of an unmanned aerial vehicle [C ] // Proceedings 2002 IEEE International Conference on Robotics and Automation . Piscataway : IEEE , 2002 : 2799 - 2804 .

KONG W W , ZHOU D L , ZHANG D B , et al . Vision-based autonomous landing system for unmanned aerial vehicle: A survey [C ] // 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems . Piscataway : IEEE , 2014 : 1 - 8 .

REN S Q , HE K M , GIRSHICK R , et al . Faster R-CNN: Towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once: Unified, real-time object detection [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2016 : 779 - 788 .

KHANAM R , HUSSAIN M . What is YOLOv5: A deep look into the internal features of the popular object detector [EB/OL ] . ( 2024-07-30 )[ 2025-10-10 ] . https://arXiv.org/abs/2407.20892 https://arXiv.org/abs/2407.20892 .

VARGHESE R , M S . YOLOv8: A novel object detection algorithm with enhanced performance and robustness [C ] // 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems . Piscataway : IEEE , 2024 : 1 - 6 .

TIAN Y J , YE Q X , DOERMANN D . YOLOv12: Attention-centric real-time object detectors [EB/OL ] . ( 2025-02-18 )[ 2025-10-10 ] . https://arXiv.org/abs/2502.12524 https://arXiv.org/abs/2502.12524 .

ZHANG Z , CHEN L , WANG Q F , et al . Monocular visual pose measurement for autonomous landing in unknown environments [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2025 , 35 ( 8 ): 7592 - 7604 .

DING J , XUE N , XIA G S , et al . Object detection in aerial images: A large-scale benchmark and challenges [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 11 ): 7778 - 7796 .

LIN T Y , DOLLÁR P , GIRSHICK R , et al . Feature pyramid networks for object detection [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2017 : 936 - 944 .

WANG H , SONG Z L . Improved mosaic: Algorithms for more complex images [J ] . Journal of Physics: Conference Series , 2020 , 1684 ( 1 ): 012094 .

NEPAL U , ESLAMIAT H . Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs [J ] . Sensors , 2022 , 22 ( 2 ): 464 .

JIANG P Y , ERGU D J , LIU F Y , et al . A review of yolo algorithm developments [J ] . Procedia Computer Science , 2022 , 199 : 1066 - 1073 .

LIU W , ANGUELOV D , ERHAN D , et al . SSD: Single shot MultiBox detector [C ] // Computer Vision - ECCV 2016 . Cham : Springer , 2016 : 21 - 37 .

PARK C H , AHN S M . Autonomous landing of drones using deep learning GPS-denied environments [C ] // Proceedings of the Korean Society of Computer Information Conference . Daejeon : Korean Society of Computer Information , 2023 : 15 - 18 .

JIANG C C , REN H Z , YE X , et al . Object detection from UAV thermal infrared images and videos using YOLO models [J ] . International Journal of Applied Earth Observation and Geoinformation , 2022 , 112 : 102912 .

SERRANO K K D , BANDALA A A . YOLO-based terrain classification for UAV safe landing zone detection [C ] // 2023 IEEE Region 10 Symposium . Piscataway : IEEE , 2023 : 1 - 5 .

ROLLAND E G A , GRØNTVED K A R , CHRISTENSEN A L , et al . Autonomous UAV volcanic plume sampling based on machine vision and path planning [C ] // 2024 International Conference on Unmanned Aircraft Systems . Piscataway : IEEE , 2024 : 1064 - 1071 .

YUAN B X , MA W Y , WANG F . High speed safe autonomous landing marker tracking of fixed wing drone based on deep learning [J ] . IEEE Access , 2022 , 10 : 80415 - 80436 .

伍瀚 , 孙浩 , 计科峰 , 等 . 时序信息引导跨视角特征融合的多无人机多目标跟踪方法 [J ] . 电子学报 , 2025 , 53 ( 3 ): 728 - 743 .

WU H , SUN H , JI K F , et al . Temporal-guided cross-view feature fusion network for multi-drone multi-object tracking [J ] . Acta Electronica Sinica , 2025 , 53 ( 3 ): 728 - 743 . (in Chinese)

LONG J , SHELHAMER E , DARRELL T . Fully convolutional networks for semantic segmentation [C ] // 2015 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2015 : 3431 - 3440 .

RONNEBERGER O , FISCHER P , BROX T . U-Net: Convolutional networks for biomedical image segmentation [C ] // Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 . Cham : Springer , 2015 : 234 - 241 .

YANG L J , YE J , ZHANG Y , et al . A semantic SLAM-based method for navigation and landing of UAVs in indoor environments [J ] . Knowledge-Based Systems , 2024 , 293 : 111693 .

CLAUDET T , TOMITA K , HO K . Benchmark analysis of semantic segmentation algorithms for safe planetary landing site selection [J ] . IEEE Access , 2022 , 10 : 41766 - 41775 .

PUTRANTO H Y , NUR IRFANSYAH A , ATTAMIMI M . Identification of safe landing areas with semantic segmentation and contour detection for delivery UAV [C ] // 2022 9th International Conference on Information Technology, Computer, and Electrical Engineering . Piscataway : IEEE , 2022 : 254 - 257 .

LOERA-PONCE J A , MERCADO-RAVELL D A , BECERRA I , et al . Risk assessment for UAV autonomous landing in urban environments using semantic segmentation [M ] // Advances in Artificial Intelligence - IBERAMIA 2024 . Cham : Springer Nature Switzerland , 2025 : 197 - 208 .

BENJWAL A , UDAY P , VADDURI A , et al . Safe landing zone detection for UAVs using image segmentation and super resolution [C ] // 2023 18th International Conference on Machine Vision and Applications . Piscataway : IEEE , 2023 : 1 - 6 .

RYU H , LIM J , LEE H J , et al . An estimation method for vision-based autonomous landing system for fixed wing aircraft [J ] . The International Journal of Robotics Research , 2025 , 44 ( 6 ): 952 - 971 .

SEFIDGAR M , LANDRY R . Unstable landing platform pose estimation based on camera and range sensor homogeneous fusion (CRHF) [J ] . Drones , 2022 , 6 ( 3 ): 60 .

BALDINI F , ANANDKUMAR A , MURRAY R M . Learning pose estimation for UAV autonomous navigation and landing using visual-inertial sensor data [C ] // 2020 American Control Conference . Piscataway : IEEE , 2020 : 2961 - 2966 .

DE CROON G C H E , HO H W , DE WAGTER C , et al . Optic-flow based slope estimation for autonomous landing [J ] . International Journal of Micro Air Vehicles , 2013 , 5 ( 4 ): 287 - 297 .

WANG P K , WU L , QI J X , et al . Unmanned aerial vehicles object detection based on image haze removal under sea fog conditions [J ] . IET Image Processing , 2022 , 16 ( 10 ): 2709 - 2721 .

DUTRANNOIS T , NGUYEN T T , HAMESSE C , et al . Visual SLAM for autonomous drone landing on a maritime platform [C ] // 2022 International Symposium on Measurement and Control in Robotics . Piscataway : IEEE , 2022 : 1 - 7 .

LIN Y X , LAI Y C . Deep learning-based navigation system for automatic landing approach of fixed-wing UAVs in GNSS-denied environments [J ] . Aerospace , 2025 , 12 ( 4 ): 324 .

吴鹏飞 , 石章松 , 黄隽 , 等 . 基于改进SSD网络的着舰标志识别方法 [J ] . 电光与控制 , 2022 , 29 ( 1 ): 88 - 92 .

WU P F , SHI Z S , HUANG J , et al . Landing mark identification method based on improved SSD network [J ] . Electronics Optics & Control , 2022 , 29 ( 1 ): 88 - 92 . (in Chinese)

LIU F , SHAN J Y , XIONG B Y , et al . A real-time and multi-sensor-based landing area recognition system for UAVs [J ] . Drones , 2022 , 6 ( 5 ): 118 .

YI S , LI J J , JIANG G , et al . CCTseg: A cascade composite transformer semantic segmentation network for UAV visual perception [J ] . Measurement , 2023 , 211 : 112612 .

DHAMI H S , IGNATYEV D , TSOURDOS A . Semantic segmentation based mapping systems for the safe and precise landing of flying vehicles [J ] . IFAC-PapersOnLine , 2022 , 55 ( 22 ): 310 - 315 .

ZHUANG J D , CHEN X , DAI M , et al . A semantic guidance and transformer-based matching method for UAVs and satellite images for UAV geo-localization [J ] . IEEE Access , 2022 , 10 : 34277 - 34287 .

XU Y , ZHONG D S , ZHOU J H , et al . A novel UAV visual positioning algorithm based on A-YOLOX [J ] . Drones , 2022 , 6 ( 11 ): 362 .

CHEN R B , XU Y , BIN SINAL M S , et al . Swin-YOLOX for autonomous and accurate drone visual landing [J ] . IET Image Processing , 2024 , 18 ( 14 ): 4731 - 4744 .

ZHANG Y , LIU X , XIAO C S , et al . Research on autonomous landing method of shipborne uncrewed aerial vehicle based on visual recognition [J ] . IEEE Access , 2025 , 13 : 179994 - 180005 .

DAI W , ZHAI Z J , WANG D Z , et al . YOMO-runwaynet: A lightweight fixed-wing aircraft runway detection algorithm combining YOLO and MobileRunwaynet [J ] . Drones , 2024 , 8 ( 7 ): 330 .

JIANG B , CHEN Z H , TAN J T , et al . A real-time semantic segmentation method based on STDC-CT for recognizing UAV emergency landing zones [J ] . Sensors , 2023 , 23 ( 14 ): 6514 .

ZHANG Q T , XIA Q Y , WEI L S , et al . A vision-based method for UAV autonomous landing area detection [C ] // Intelligence Science V . Cham : Springer , 2025 : 204 - 213 .

MUMUNI A , MUMUNI F . Data augmentation: A comprehensive survey of modern approaches [J ] . Array , 2022 , 16 : 100258 .

TRUONG N Q , LEE Y W , OWAIS M , et al . SlimDeblurGAN-based motion deblurring and marker detection for autonomous drone landing [J ] . Sensors , 2020 , 20 ( 14 ): 3918 .

NAUFAL C , SOLANO-CORREA Y T , MARRUGO A G . YOLO-based multi-scale ground control point detection in UAV surveying [C ] // 2023 IEEE Colombian Caribbean Conference (C3) . Piscataway : IEEE , 2024 : 1 - 5 .

LEE M , SHIN S G , JANG S , et al . Visual-based landing guidance system of UAV with deep learning technique for environments of visual-detection impairment [J ] . International Journal of Control, Automation and Systems , 2022 , 20 ( 5 ): 1735 - 1744 .

LIM J , KIM M , YOO H , et al . Autonomous multirotor UAV search and landing on safe spots based on combined semantic and depth information from an onboard camera and LiDAR [J ] . IEEE/ASME Transactions on Mechatronics , 2024 , 29 ( 5 ): 3960 - 3970 .

ZHANG Z Q , ZHANG Y F , XIANG S , et al . KDP-net: An efficient semantic segmentation network for emergency landing of unmanned aerial vehicles [J ] . Drones , 2024 , 8 ( 2 ): 46 .

XUE C , XIA Y L , WU M J , et al . EL-YOLO: An efficient and lightweight low-altitude aerial objects detector for onboard applications [J ] . Expert Systems with Applications , 2024 , 256 : 124848 .

张琴 . 基于视觉辅助定位的无人机智能降落系统研究与实现 [D ] . 南京 : 南京邮电大学 , 2020 .

ZHANG Q . Research and Implementation of UAV Intelligent Landing System Based on Vision-Assisted Positioning [D ] . Nanjing : Nanjing University of Posts and Telecommunications , 2020 . (in Chinese)

YU L J , LUO C , YU X R , et al . Deep learning for vision-based micro aerial vehicle autonomous landing [J ] . International Journal of Micro Air Vehicles , 2018 , 10 ( 2 ): 171 - 185 .

DAVIS J , GOADRICH M . The relationship between precision-recall and ROC curves [C ] // Proceedings of the 23rd International Conference on Machine Learning . New York : ACM , 2006 : 233 - 240 .

ZHENG Z H , WANG P , LIU W , et al . Distance-IoU loss: Faster and better learning for bounding box regression [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 7 ): 12993 - 13000 .

DU D W , WEN L Y , ZHU P F , et al . VisDrone-DET2020: The vision meets drone object detection in image challenge results [C ] // Computer Vision - ECCV 2020 Workshops . Cham : Springer , 2020 : 692 - 712 .

LYU Y , VOSSELMAN G , XIA G S , et al . UAVid: A semantic segmentation dataset for UAV imagery [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 165 : 108 - 119 .

SUN H M , GUO J C , MENG Z B , et al . EVD4UAV: An altitude-sensitive benchmark to evade vehicle detection in UAV [C ] // 2024 IEEE Intelligent Vehicles Symposium . Piscataway : IEEE , 2024 : 545 - 552 .

LI T J , LIU J , ZHANG W , et al . UAV-human: A large benchmark for human behavior understanding with unmanned aerial vehicles [C ] // 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2021 : 16261 - 16270 .

XIA G S , HU J W , HU F , et al . AID: A benchmark data set for performance evaluation of aerial scene classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2017 , 55 ( 7 ): 3965 - 3981 .

MUELLER M , SMITH N , GHANEM B . A benchmark and simulator for UAV tracking [C ] // Computer Vision - ECCV 2016 . Cham : Springer , 2016 : 445 - 461 .

汪进中 , 戴顺 , 张秀伟 , 等 . 无人机视角多源目标检测数据集UAV-RGBT及算法基准 [J ] . 电子学报 , 2025 , 53 ( 3 ): 686 - 704 .

WANG J Z , DAI S , ZHANG X W , et al . UAV-RGBT multispectral object detection dataset and algorithm benchmark [J ] . Acta Electronica Sinica , 2025 , 53 ( 3 ): 686 - 704 . (in Chinese)

HAUSMANN P , MEESS H , ELGER G . Image segmentation based emergency landing for autonomous and automated unmanned aerial vehicles [EB/OL ] .( 2022 )[ 2025-10-10 ] . https://www.icas.org/icas_archive/ICAS2022/data/papers/ICAS2022_0725_paper.pdf https://www.icas.org/icas_archive/ICAS2022/data/papers/ICAS2022_0725_paper.pdf .

NGUYEN P H , ARSALAN M , KOO J H , et al . LightDenseYOLO: A fast and accurate marker tracker for autonomous UAV landing by visible light camera sensor on drone [J ] . Sensors , 2018 , 18 ( 6 ): 1703 .

HINNIGER C , RÜTER J . Synthetic training data for semantic segmentation of the environment from UAV perspective [J ] . Aerospace , 2023 , 10 ( 7 ): 604 .

GRLJ C G , KRZNAR N , PRANJIĆ M . A decade of UAV docking stations: A brief overview of mobile and fixed landing platforms [J ] . Drones , 2022 , 6 ( 1 ): 17 .

SHAH S , DEY D , LOVETT C , et al . AirSim: High-fidelity visual and physical simulation for autonomous vehicles [C ] // Field and Service Robotics . Cham : Springer , 2018 : 621 - 635 .

SHAHAM T R , SCHWETTMANN S , WANG F , et al . A multimodal automated interpretability agent [EB/OL ] . ( 2025-02-11 )[ 2025-10-20 ] . https://arXiv.org/abs/2404.14394 https://arXiv.org/abs/2404.14394 .

HE H Q , LI C C , YANG R H , et al . Multisource data fusion and adversarial nets for landslide extraction from UAV-photogrammetry-derived data [J ] . Remote Sensing , 2022 , 14 ( 13 ): 3059 .

HOWARD A , SANDLER M , CHEN B , et al . Searching for MobileNetV3 [C ] // 2019 IEEE/CVF International Conference on Computer Vision . Piscataway : IEEE , 2019 : 1314 - 1324 .

FAN B K , LI Y , ZHANG R Y , et al . Review on the technological development and application of UAV systems [J ] . Chinese Journal of Electronics , 2020 , 29 ( 2 ): 199 - 207 .

HU Y C , MENG W . ROSUnitySim: Development and experimentation of a real-time simulator for multi-unmanned aerial vehicle local planning [J ] . Simulation , 2016 , 92 ( 10 ): 931 - 944 .

BAIDYA R , JEONG H . Simulation and real-life implementation of UAV autonomous landing system based on object recognition and tracking for safe landing in uncertain environments [J ] . Frontiers in Robotics and AI , 2024 , 11 : 1450266 .

KAKALETSIS E , SYMEONIDIS C , TZELEPI M , et al . Computer vision for autonomous UAV flight safety: An overview and a vision-based safe landing pipeline example [J ] . ACM Computing Surveys , 2021 , 54 ( 9 ): 1 - 37 .

王巍 , 解慧 , 魏忠诚 , 等 . 不确定需求下无人机任务分配的两阶段鲁棒优化方法 [J ] . 电子学报 , 2024 , 52 ( 10 ): 3552 - 3561 .

WANG W , XIE H , WEI Z C , et al . Two-stage robust optimization method for UAV task assignment under uncertain demand [J ] . Acta Electronica Sinica , 2024 , 52 ( 10 ): 3552 - 3561 . (in Chinese)

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