Deep Learning-Based Unmanned Aerial Vehicle (UAV)
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".
Deadline for manuscript submissions: 20 July 2026
Special Issue Editors
Interests: intelligent decision-making in multimodal unmanned systems
Special Issue Information
Dear Colleagues,
The integration of Unmanned Aerial Vehicles (UAVs) with deep learning (DL) has catalyzed a paradigm shift, transforming drones from remotely piloted platforms into intelligent, autonomous systems capable of perceiving, reasoning, and acting in complex environments. This convergence is unlocking unprecedented capabilities across a myriad of applications, pushing the boundaries of what is possible in aerial robotics. The primary objective of this Special Issue is to collate cutting-edge research that explores the synergistic relationship between advanced DL methodologies and UAV technology, addressing both foundational challenges and novel applications.
The scope of this Special Issue encompasses a broad spectrum of topics where DL algorithms serve as the core intelligence for UAV operations. A key area of focus is autonomous navigation and control, including deep reinforcement learning for agile flight in GPS-denied environments, robust obstacle avoidance in dynamic settings, and secure multi-UAV path planning. Furthermore, we seek contributions in perception and sensing, particularly real-time object detection, tracking, and semantic segmentation using convolutional neural networks (CNNs) and vision transformers applied to aerial imagery. This extends to challenging tasks such as 3D scene reconstruction and multi-modal sensor fusion (e.g., LiDAR, visual, and infrared).
Beyond core algorithmic development, this Special Issue will highlight the critical challenge of computational efficiency; therefore, we welcome research on model compression, knowledge distillation, and the design of lightweight neural networks that enable the deployment of sophisticated DL models on the constrained hardware of UAVs, facilitating real-time edge intelligence.
Finally, this Special Issue will showcase transformative real-world applications powered by DL-driven UAVs. This includes, but is not limited to, precision agriculture for crop monitoring and yield prediction, intelligent infrastructure inspection, automated search and rescue operations, enhanced surveillance and public safety, and innovative environmental monitoring. We invite contributions that not only demonstrate technical novelty but also provide tangible validation and discuss the practical implications of the proposed systems.
By bringing together diverse research threads, this Special Issue aims to provide a comprehensive overview of the state-of-the-art, foster interdisciplinary collaboration, and chart a course for the future of intelligent, autonomous unmanned systems.
Dr. Kun Wu
Dr. Huaxin Qiu
Guest Editors
Manuscript Submission Information
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Keywords
- deep learning
- autonomous navigation
- object detection and tracking
- UAV mission decision-making
- multi-UAV systems
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