Abstract
Significant progress has been made in the field of deep learning through intensive research over the last decade. So-called convolutional neural networks are an essential component of this research. In this type of neural network, the mathematical convolution operator is used to extract characteristics or anomalies. The purpose of this work is to investigate the extent to which it is possible in certain initial settings to input aerial recordings and flight data of Unmanned Aerial Vehicles (UAVs) in the architecture of a neural network and to detect and map an object. Using the calculated contours or dimensions of the so-called bounding boxes, the position of the objects can be determined relative to the current UAV location.
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Acknowledgments
The authors would like to thank the Research and Development Sector at the Technical University of Sofia for the financial support.
This research is partly supported by the Bulgarian National Science Fund in the scope of the project “Exploration the application of statistics and machine learning in electronics” under contract number КП-06-H42/1.
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Hensel, S., Marinov, M.B., Schmitt, M. (2021). Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 382. Springer, Cham. https://doi.org/10.1007/978-3-030-78459-1_19
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