Research Article
Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks
@INPROCEEDINGS{10.1007/978-3-030-78459-1_19, author={Stefan Hensel and Marin B. Marinov and Max Schmitt}, title={Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks}, proceedings={Future Access Enablers for Ubiquitous and Intelligent Infrastructures. 5th EAI International Conference, FABULOUS 2021, Virtual Event, May 6--7, 2021, Proceedings}, proceedings_a={FABULOUS}, year={2021}, month={6}, keywords={Computer vision Object detection Deep learning Convolutional neural network}, doi={10.1007/978-3-030-78459-1_19} }- Stefan Hensel
Marin B. Marinov
Max Schmitt
Year: 2021
Object Detection and Mapping with Unmanned Aerial Vehicles Using Convolutional Neural Networks
FABULOUS
Springer
DOI: 10.1007/978-3-030-78459-1_19
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.
