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Drones
Special Issues
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 October 2024 | Viewed by 1341

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

Published Papers (1 paper)

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Research

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 - 08 Apr 2024
Viewed by 776
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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