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Perception and Control Technology for Intelligent Autonomous Unmanned Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 15 August 2025 | Viewed by 433

Special Issue Editors


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Guest Editor
School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
Interests: visual navigation of UAV; image processing; target tracking and recognition
Special Issues, Collections and Topics in MDPI journals
College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: object detection; artificial intelligence; vision navigation; image fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An autonomous unmanned system (AUS) is an electromechanical system that can perform a specified task under its own power autonomously. Such systems encompass unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs) and unmanned underwater vehicles (UUVs). The development of artificial intelligence technologies can enhance their capabilities and form intelligent autonomous unmanned systems (iAUSs). iAUS is an interdisciplinary field that relies on advances in big data, artificial intelligence and other sciences and technological domains to create autonomous unmanned systems with integrated mission abilities, motion planning, decision making and reasoning capabilities that are intelligent, autonomous and collaborative.

Target detection, tracking, recognition, positioning and other sensing technologies, as well as system navigation and guidance (and other control technologies), are the most basic technologies of iAUSs. At present, these kinds of technologies also show a variety of intelligent development characteristics. This Special Issue aims to discuss the technologies involved in iAUSs, present the latest research advancements and facilitate the exchange of information.

Scholars in the field of unmanned system perception and control are invited to present research results, exchange scientific research experience and contribute to the development of unmanned system technology research.

Prof. Dr. Chunhui Zhao
Dr. Shuai Hao
Guest Editors

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Keywords

  • environment perception
  • intelligent network
  • target detection
  • target recognition
  • target tracking
  • reactive control
  • perception-aware control
  • SLAM
  • robot
  • event camera
  • point cloud processing
  • image processing
  • information fusion

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Published Papers (1 paper)

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Research

16 pages, 5727 KiB  
Article
ED-Swin Transformer: A Cassava Disease Classification Model Integrated with UAV Images
by Jing Zhang, Hao Zhou, Kunyu Liu and Yuguang Xu
Sensors 2025, 25(8), 2432; https://doi.org/10.3390/s25082432 - 12 Apr 2025
Viewed by 273
Abstract
The outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers advantages such as high resolution [...] Read more.
The outbreak of cassava diseases poses a serious threat to agricultural economic security and food production systems in tropical regions. Traditional manual monitoring methods are limited by efficiency bottlenecks and insufficient spatial coverage. Although low-altitude drone technology offers advantages such as high resolution and strong timeliness, it faces dual challenges in the field of disease identification, such as complex background interference and irregular disease morphology. To address these issues, this study proposes an intelligent classification method for cassava diseases based on drone imagery and an ED-Swin Transformer. Firstly, we introduced the EMAGE (Efficient Multi-Scale Attention with Grouping and Expansion) module, which integrates the global distribution features and local texture details of diseased leaves in drone imagery through a multi-scale grouped attention mechanism, effectively mitigating the interference of complex background noise on feature extraction. Secondly, the DASPP (Deformable Atrous Spatial Pyramid Pooling) module was designed to use deformable atrous convolution to adaptively match the irregular boundaries of diseased areas, enhancing the model’s robustness to morphological variations caused by angles and occlusions in low-altitude drone photography. The results show that the ED-Swin Transformer model achieved excellent performance across five evaluation metrics, with scores of 94.32%, 94.56%, 98.56%, 89.22%, and 96.52%, representing improvements of 1.28%, 2.32%, 0.38%, 3.12%, and 1.4%, respectively. These experiments demonstrate the superior performance of the ED-Swin Transformer model in cassava classification networks. Full article
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