Deep Learning Methods and Applications for Unmanned Aerial Vehicles
This special issue belongs to the section "Electrical and Autonomous Vehicles".
Special Issue Information
Dear Colleagues,
Over the recent years, unmanned aerial vehicles (UAVs) have become more widespread, as they have been adopted to various practical applications, including aerial photography, surveillance, disaster relief, rescue missions, cargo delivery, and even air travels. These applications require accurate perception of the environments and self-control for successful completion of the tasks. While deep-learning-based AI systems have obtained impressive performance on various perception and control tasks, their large memory and computational requirements make it difficult for them to be applied to devices with limited computing power, such as embedded GPU systems on UAVs. Moreover, UAVs’ unique operating environment gives rise to new problems that are not found in conventional environments (e.g., small object detection, 3D navigation, lack of training data, multi-agent learning).
In this Special Issue on “Deep Learning Method and Application for Unmanned Aerial Vehicles”, we plan to tackle such practical difficulties that exist with applying deep learning-based AI systems to UAVs.
Dr. Sung Ju Hwang
Guest Editor
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Keywords
- Model compression (e.g., pruning, quantization, and knowledge distillation)
- Energy-efficient deep learning (e.g., spiking neural networks)
- Real-time computer vision (e.g., object detection, semantic segmentation, and object tracking)
- Real-time continuous control with reinforcement learning
- Multi-agent reinforcement learning
- Low-resource (data) learning (e.g., learning with limited data, domain adaptation)
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Published Papers
Electronics - ISSN 2079-9292