With advancements in swarm intelligence, artificial intelligence, and wireless mobile network technology, unmanned swarms such as unmanned aerial vehicles, ground vehicles, ships, and other unmanned systems are becoming increasingly autonomous and intelligent. Benefiting from these technologies, intelligent unmanned swarms are able to efficiently perform complex tasks through collaboration in various fields. However, malicious use of intelligent unmanned swarms raises concerns about the potential for significant damage to national infrastructures such as airports and power facilities. Defending against malicious activities is essential but challenging due to the swarms' abilities to perceive, understand complex environments, and make accurate decisions through multi-system collaboration. This perspective sheds light on recent research in counter-measures and provides new trends and insights on how to prevent malicious actions by intelligent unmanned swarms.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10018545 | PMC |
http://dx.doi.org/10.1016/j.xinn.2023.100396 | DOI Listing |
PLoS One
January 2025
North China Institute of Aerospace Engineering, Langfang, China.
As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively.
View Article and Find Full Text PDFNat Commun
January 2025
State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Institute of Technology, Anhui Agricultural University, Hefei, China.
Introduction: The rapid urbanization of rural regions, along with an aging population, has resulted in a substantial manpower scarcity for agricultural output, necessitating the urgent development of highly intelligent and accurate agricultural equipment technologies.
Methods: This research introduces YOLOv8-PSS, an enhanced lightweight obstacle detection model, to increase the effectiveness and safety of unmanned agricultural robots in intricate field situations. This YOLOv8-based model incorporates a depth camera to precisely identify and locate impediments in the way of autonomous agricultural equipment.
PLoS One
January 2025
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi, P.R. China.
Automated large-scale farmland preparation operations face significant challenges related to path planning efficiency and uniformity in resource allocation. To improve agricultural production efficiency and reduce operational costs, an enhanced method for planning land preparation paths is proposed. In the initial stage, unmanned aerial vehicles (UAVs) are employed to collect data from the field, which is then used to construct accurate farm models.
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