期刊名：Drones

期刊主页：https://www.mdpi.com/journal/drones

随着人工智能技术的快速发展，无人机正从传统的遥控操作向全自主智能系统演进，通过集成深度学习、计算机视觉和强化学习等算法，实现复杂环境下的实时感知、决策与路径规划。AI赋能的无人机也已广泛应用于精准农业、基础设施巡检、应急救援和自主物流等领域，显著提升了任务执行效率与安全性。

1.

Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles

基于深度强化学习的无人机视觉导航避障方法

https://www.mdpi.com/2504-446X/7/4/245

Kalidas, A.P.; Joshua, C.J.; Md, A.Q.; Basheer, S.; Mohan, S.; Sakri, S. Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles. Drones 2023, 7, 245.

2.

Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review

面向多无人机系统的可扩展和协作式深度强化学习方法：系统性综述

https://www.mdpi.com/2504-446X/7/4/236

Frattolillo, F.; Brunori, D.; Iocchi, L. Scalable and Cooperative Deep Reinforcement Learning Approaches for Multi-UAV Systems: A Systematic Review. Drones 2023, 7, 236.

3.

Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture

变革农业：人工智能无人机技术在精准农业中的应用综述

https://www.mdpi.com/2504-446X/8/11/664

Agrawal, J.; Arafat, M.Y. Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture. Drones 2024, 8, 664.

4.

Real-Time Fire Detection: Integrating Lightweight Deep Learning Models on Drones with Edge Computing

实时火灾探测：将轻量级深度学习模型与无人机边缘计算相结合

http://www.mdpi.com/2504-446X/8/9/483

Titu, M.F.S.; Pavel, M.A.; Michael, G.K.O.; Babar, H.; Aman, U.; Khan, R. Real-Time Fire Detection: Integrating Lightweight Deep Learning Models on Drones with Edge Computing. Drones 2024, 8, 483.

5.

A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal

基于无人机360°相机和三轴云台的深度学习入侵检测与跟踪

https://www.mdpi.com/2504-446X/8/2/68

Xu, Y.; Liu, Y.; Li, H.; Wang, L.; Ai, J. A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal. Drones 2024, 8, 68.

6.

Deep Learning for Indoor Pedestal Fan Blade Inspection: Utilizing Low-Cost Autonomous Drones in an Educational Setting

深度学习在室内落地扇叶片检测中的应用：在教育环境中利用低成本自主无人机

https://www.mdpi.com/2504-446X/8/7/298

Rodriguez, A.A.; Davis, M.; Zander, J.; Nazario Dejesus, E.; Shekaramiz, M.; Memari, M.; Masoum, M.A.S. Deep Learning for Indoor Pedestal Fan Blade Inspection: Utilizing Low-Cost Autonomous Drones in an Educational Setting. Drones 2024, 8, 298.

7.

UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review

无人机嵌入式传感器和深度学习在建筑立面病理识别中的应用：综述

https://www.mdpi.com/2504-446X/8/7/341

Meira, G.d.S.; Guedes, J.V.F.; Bias, E.d.S. UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review. Drones 2024, 8, 341.

8.

Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms

提高空中目标定位精度：基于高级深度学习算法的点云语义分割研究

https://www.mdpi.com/2504-446X/8/8/376

Bozkurt, S.; Atik, M.E.; Duran, Z. Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms. Drones 2024, 8, 376.

9.

A Review on Deep Learning for UAV Absolute Visual Localization

无人机绝对视觉定位深度学习综述

https://www.mdpi.com/2504-446X/8/11/622

Couturier, A.; Akhloufi, M.A. A Review on Deep Learning for UAV Absolute Visual Localization. Drones 2024, 8, 622.

10.

Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon

基于深度学习的自主水下航行器对接方案，配备全向旋转光学信标

https://www.mdpi.com/2504-446X/8/12/697

Li, Y.; Sun, K.; Han, Z.; Lang, J. Deep Learning-Based Docking Scheme for Autonomous Underwater Vehicles with an Omnidirectional Rotating Optical Beacon. Drones 2024, 8, 697.

11.

An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles

基于人工智能的深度学习与K均值算法在提高无人机高度估计精度方面的应用

https://www.mdpi.com/2504-446X/8/12/718

Piyakawanich, P.; Phasukkit, P. An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles. Drones 2024, 8, 718.

12.

Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning

利用机载视觉传感器和机器学习实现无人地面车辆的稳健编队控制

https://www.mdpi.com/2504-446X/8/12/787

Li, M.; Liu, H.; Xie, F. Robust Formation Control for Unmanned Ground Vehicles Using Onboard Visual Sensors and Machine Learning. Drones 2024, 8, 787.

13.

DEGNN: A Deep Learning-Based Method for Unmanned Aerial Vehicle Software Security Analysis

DEGNN：一种基于深度学习的无人机软件安全分析方法

https://www.mdpi.com/2504-446X/9/2/110

Du, J.; Wei, Q.; Wang, Y.; Bai, X. DEGNN: A Deep Learning-Based Method for Unmanned Aerial Vehicle Software Security Analysis. Drones 2025, 9, 110.

14.

GRU-Based Deep Learning Framework for Real-Time, Accurate, and Scalable UAV Trajectory Prediction

基于GRU的深度学习框架，用于实时、精确和可扩展的无人机轨迹预测

https://www.mdpi.com/2504-446X/9/2/142

Yoon, S.; Jang, D.; Yoon, H.; Park, T.; Lee, K. GRU-Based Deep Learning Framework for Real-Time, Accurate, and Scalable UAV Trajectory Prediction. Drones 2025, 9, 142.

15.

UAV Localization in Urban Area Mobility Environment Based on Monocular VSLAM with Deep Learning

基于单目VSLAM和深度学习的城市区域移动环境下的无人机定位

https://www.mdpi.com/2504-446X/9/3/171

Norbelt, M.; Luo, X.; Sun, J.; Claude, U. UAV Localization in Urban Area Mobility Environment Based on Monocular VSLAM with Deep Learning. Drones 2025, 9, 171.

期刊介绍

主编：Prof. Dr. Diego González-Aguilera

Drones是一个国际性的、同行评审与开放获取的期刊，专注于无人机（包括无人驾驶飞行器 (UAV)、无人飞行器系统 (UAS)、遥控驾驶飞行器系统 (RPAS) 等）的设计和应用，以及无人海洋/水上/水下无人机、无人地面车辆、全自主驾驶和太空无人机的设计和应用，由 MDPI 每月在线出版。

2024 Impact Factor：4.8

2025 CiteScore：10.0

Time to First Decision：20.8 Days

Acceptance to Publication： 2.7 Days