Abstract:Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge s… Show more
“…Machine Learning algorithms are now widely used in perception and computer vision, especially for classification and decision making. In [6], a light-weight deep neural network architecture is proposed for real-time object classification, considering mission specific input data augmentation techniques. In [7], a classifier is designed for aerial images via deep transfer learning for UAV networks.…”
Unmanned Aerial Vehicles (UAVs) are recognized as very useful tools to replace, help, or assist humans in various missions, such as inspection and monitoring, surveillance, search and rescue, exploration, logistics and transportation, etc [...]
“…Machine Learning algorithms are now widely used in perception and computer vision, especially for classification and decision making. In [6], a light-weight deep neural network architecture is proposed for real-time object classification, considering mission specific input data augmentation techniques. In [7], a classifier is designed for aerial images via deep transfer learning for UAV networks.…”
Unmanned Aerial Vehicles (UAVs) are recognized as very useful tools to replace, help, or assist humans in various missions, such as inspection and monitoring, surveillance, search and rescue, exploration, logistics and transportation, etc [...]
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