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Drones
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Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology
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Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 12881

Special Issue Editors

Dr. Zhaodan Kong

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of California, Davis, CA 95616, USA
Interests: control theory; machine learning; formal methods; and their applications to autonomous systems; human-autonomy teaming; cyber-physical systems; neural engineering
Dr. Peng Wei

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA
Interests: robotics; unmanned aerial/ground systems; optimal planning and control; machine learning
Dr. William J. Beksi

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
Interests: robot perception; human-robot interaction; autonomous systems
Dr. Dongfang Liu

E-Mail Website
Guest Editor
Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
Interests: human-embodied language-vision intelligence

Special Issue Information

Dear Colleagues, 

Uncrewed aerial vehicles (UAVs), commonly known as drones, have witnessed a significant surge in applications across various industries over the past few decades. Examples include agriculture, construction, disaster response, environmental monitoring, public safety, and defense. Integrating intelligence (AI) and machine learning (ML) with UAV technology is a key element contributing to this surge. This integration significantly enhances the capability of UAVs in various aspects, such as object recognition, autonomous navigation, obstacle avoidance, real-time decision, and teaming. It transforms UAVs from remote-controlled devices to intelligent and adaptive systems capable of performing various complex tasks across multiple domains. As the integration continues to advance, the synergy between UAVs and AI/ML will likely lead to further innovations and expanded applications. 

This Special Issue is dedicated to exploring the integration of AI and ML with UAV technology, focusing on current innovations and future trends. Through this Special Issue, we aspire to foster a dynamic exchange of ideas and collaborations between academic researchers and industry practitioners. Our goal is to spotlight the significant strides in, on the one hand, basic research on enhancing/enabling UAV capabilities with AI and ML, and, on the other hand, applied research on the broader adoption and application of UAVs across diverse fields with the help of AI and ML. 

Topics for submission include, but are not limited to:

  • AI/ML-driven UAV Perception and Object Detection/Tracking
  • AI/ML-driven UAV Localization and Navigation
  • AI/ML-driven Decision Making in UAV Operations
  • AI/ML-driven UAV Trajectory and Motion Planning
  • AI/ML-driven UAV Control
  • AI/ML-driven Swarm Coordination for UAVs
  • AI/ML-assisted Geospatial Mapping with UAVs
  • AI/ML-assisted Precision Agriculture with UAVs
  • AI/ML-assisted Environmental Monitoring with UAVs

Dr. Zhaodan Kong
Dr. Peng Wei
Dr. William J. Beksi
Dr. Dongfang Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • UAV
  • object detection and tracking
  • localization and navigation
  • decision making
  • planning and control
  • swarm coordination
  • geospatial mapping
  • precision agriculture
  • environmental monitoring

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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22 pages, 1098 KiB  
Article
Enhanced Link Prediction and Traffic Load Balancing in Unmanned Aerial Vehicle-Based Cloud-Edge-Local Networks
by Hao Long, Feng Hu and Lingjun Kong
Drones 2024, 8(10), 528; https://doi.org/10.3390/drones8100528 - 27 Sep 2024
Viewed by 1017
Abstract
With the advancement of cloud-edge-local computing, Unmanned Aerial Vehicles (UAVs), as flexible mobile nodes, offer novel solutions for dynamic network deployment. However, existing research on UAV networks faces substantial challenges in accurately predicting link dynamics and efficiently managing traffic loads, particularly in highly [...] Read more.
With the advancement of cloud-edge-local computing, Unmanned Aerial Vehicles (UAVs), as flexible mobile nodes, offer novel solutions for dynamic network deployment. However, existing research on UAV networks faces substantial challenges in accurately predicting link dynamics and efficiently managing traffic loads, particularly in highly distributed and rapidly changing environments. These limitations result in inefficient resource allocation and suboptimal network performance. To address these challenges, this paper proposes a UAV-based cloud-edge-local network resource elastic scheduling architecture, which integrates the Graph-Autoencoder–GAN-LSTM (GA–GLU) algorithm for accurate link prediction and the FlowBender-Enhanced Reinforcement Learning for Load Balancing (FERL-LB) algorithm for dynamic traffic load balancing. GA–GLU accurately predicts dynamic changes in UAV network topologies, enabling adaptive and efficient scheduling of network resources. FERL-LB leverages these predictions to optimize traffic load balancing within the architecture, enhancing both performance and resource utilization. To validate the effectiveness of GA–GLU, comparisons are made with classical methods such as CN and Katz, as well as modern approaches like Node2vec and GAE–LSTM, which are commonly used for link prediction. Experimental results demonstrate that GA–GLU consistently outperforms these competitors in metrics such as AUC, MAP, and error rate. The integration of GA–GLU and FERL-LB within the proposed architecture significantly improves network performance in highly dynamic environments. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)
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26 pages, 11965 KiB  
Article
AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images
by Sen Wang, Huiping Jiang, Jixiang Yang, Xuan Ma and Jiamin Chen
Drones 2024, 8(10), 523; https://doi.org/10.3390/drones8100523 - 26 Sep 2024
Viewed by 1167
Abstract
To address the challenge of low detection accuracy and slow detection speed in unmanned aerial vehicle (UAV) aerial images target detection tasks, caused by factors such as complex ground environments, varying UAV flight altitudes and angles, and changes in lighting conditions, this study [...] Read more.
To address the challenge of low detection accuracy and slow detection speed in unmanned aerial vehicle (UAV) aerial images target detection tasks, caused by factors such as complex ground environments, varying UAV flight altitudes and angles, and changes in lighting conditions, this study proposes an end-to-end adaptive multi-scale feature extraction and fusion detection network, named AMFEF-DETR. Specifically, to extract target features from complex backgrounds more accurately, we propose an adaptive backbone network, FADC-ResNet, which dynamically adjusts dilation rates and performs adaptive frequency awareness. This enables the convolutional kernels to effectively adapt to varying scales of ground targets, capturing more details while expanding the receptive field. We also propose a HiLo attention-based intra-scale feature interaction (HLIFI) module to handle high-level features from the backbone. This module uses dual-pathway encoding of high and low frequencies to enhance the focus on the details of dense small targets while reducing noise interference. Additionally, the bidirectional adaptive feature pyramid network (BAFPN) is proposed for cross-scale feature fusion, integrating semantic information and enhancing adaptability. The Inner-Shape-IoU loss function, designed to focus on bounding box shapes and incorporate auxiliary boxes, is introduced to accelerate convergence and improve regression accuracy. When evaluated on the VisDrone dataset, the AMFEF-DETR demonstrated improvements of 4.02% and 16.71% in mAP50 and FPS, respectively, compared to the RT-DETR. Additionally, the AMFEF-DETR model exhibited strong robustness, achieving mAP50 values 2.68% and 3.75% higher than the RT-DETR and YOLOv10, respectively, on the HIT-UAV dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)
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20 pages, 6177 KiB  
Article
Military Image Captioning for Low-Altitude UAV or UGV Perspectives
by Lizhi Pan, Chengtian Song, Xiaozheng Gan, Keyu Xu and Yue Xie
Drones 2024, 8(9), 421; https://doi.org/10.3390/drones8090421 - 24 Aug 2024
Viewed by 980
Abstract
Low-altitude unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which boast high-resolution imaging and agile maneuvering capabilities, are widely utilized in military scenarios and generate a vast amount of image data that can be leveraged for textual intelligence generation to support military [...] Read more.
Low-altitude unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which boast high-resolution imaging and agile maneuvering capabilities, are widely utilized in military scenarios and generate a vast amount of image data that can be leveraged for textual intelligence generation to support military decision making. Military image captioning (MilitIC), as a visual-language learning task, provides innovative solutions for military image understanding and intelligence generation. However, the scarcity of military image datasets hinders the advancement of MilitIC methods, especially those based on deep learning. To overcome this limitation, we introduce an open-access benchmark dataset, which was termed the Military Objects in Real Combat (MOCO) dataset. It features real combat images captured from the perspective of low-altitude UAVs or UGVs, along with a comprehensive set of captions. Furthermore, we propose a novel encoder–augmentation–decoder image-captioning architecture with a map augmentation embedding (MAE) mechanism, MAE-MilitIC, which leverages both image and text modalities as a guiding prefix for caption generation and bridges the semantic gap between visual and textual data. The MAE mechanism maps both image and text embeddings onto a semantic subspace constructed by relevant military prompts, and augments the military semantics of the image embeddings with attribute-explicit text embeddings. Finally, we demonstrate through extensive experiments that MAE-MilitIC surpasses existing models in performance on two challenging datasets, which provides strong support for intelligence warfare based on military UAVs and UGVs. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)
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31 pages, 1449 KiB  
Article
Analysis of Unmanned Aerial Vehicle-Assisted Cellular Vehicle-to-Everything Communication Using Markovian Game in a Federated Learning Environment
by Xavier Fernando and Abhishek Gupta
Drones 2024, 8(6), 238; https://doi.org/10.3390/drones8060238 - 2 Jun 2024
Cited by 5 | Viewed by 1278
Abstract
The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a [...] Read more.
The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)
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21 pages, 4821 KiB  
Article
SSMA-YOLO: A Lightweight YOLO Model with Enhanced Feature Extraction and Fusion Capabilities for Drone-Aerial Ship Image Detection
by Yuhang Han, Jizhuang Guo, Haoze Yang, Renxiang Guan and Tianjiao Zhang
Drones 2024, 8(4), 145; https://doi.org/10.3390/drones8040145 - 8 Apr 2024
Cited by 7 | Viewed by 2664
Abstract
Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity [...] Read more.
Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. First, this paper introduces a newly designed SSC2f structure that incorporates spatial and channel convolution (SCConv) and spatial group-wise enhancement (SGE) attention mechanisms. This design reduces spatial and channel redundancies within the neural network, enhancing detection accuracy while simultaneously reducing the model’s parameter count. Second, the newly designed MC2f structure employs the multidimensional collaborative attention (MCA) mechanism to efficiently model spatial and channel features, enhancing recognition efficiency in complex backgrounds. Additionally, the asymptotic feature pyramid network (AFPN) structure was designed for progressively fusing multi-level features from the backbone layers, overcoming challenges posed by multi-scale variations. Experiments of the ships dataset show that the proposed model achieved a 4.4% increase in mAP compared to the state-of-the-art single-stage target detection YOLOv8n model while also reducing the number of parameters by 23%. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)
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Review

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46 pages, 13038 KiB  
Review
A Review on Deep Learning for UAV Absolute Visual Localization
by Andy Couturier and Moulay A. Akhloufi
Drones 2024, 8(11), 622; https://doi.org/10.3390/drones8110622 - 29 Oct 2024
Viewed by 2003
Abstract
In the past few years, the use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search and rescue, and even insurance. This surge in adoption [...] Read more.
In the past few years, the use of Unmanned Aerial Vehicles (UAVs) has expanded and now reached mainstream levels for applications such as infrastructure inspection, agriculture, transport, security, entertainment, real estate, environmental conservation, search and rescue, and even insurance. This surge in adoption can be attributed to the UAV ecosystem’s maturation, which has not only made these devices more accessible and cost effective but has also significantly enhanced their operational capabilities in terms of flight duration and embedded computing power. In conjunction with these developments, the research on Absolute Visual Localization (AVL) has seen a resurgence driven by the introduction of deep learning to the field. These new approaches have significantly improved localization solutions in comparison to the previous generation of approaches based on traditional computer vision feature extractors. This paper conducts an extensive review of the literature on deep learning-based methods for UAV AVL, covering significant advancements since 2019. It retraces key developments that have led to the rise in learning-based approaches and provides an in-depth analysis of related localization sources such as Inertial Measurement Units (IMUs) and Global Navigation Satellite Systems (GNSSs), highlighting their limitations and advantages for more effective integration with AVL. The paper concludes with an analysis of current challenges and proposes future research directions to guide further work in the field. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine Learning (ML) in UAV Technology)
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