Autors: Hensel, S., Marinov, M. B., Schmitt, M.
Title: Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks
Keywords: computer vision; object detection; deep learning; convolutional neural network

Abstract: Significant progress has been made in the field of deep learning through in-tensive research over the last decade. So-called convolutional neural net-works 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 con-tours or dimensions of the so-called bounding boxes, the position of the ob-jects can be determined relative to the current UAV location.

References

    Issue

    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, pp. 254–267, 2021, Croatia, Springer, Cham, https://doi.org/10.1007/978-3-030-78459-1_19

    Copyright Springer

    Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science