@inproceedings{f02a8d68f6544024ac0f501fab42bb9a,
title = "Embedded vision system for automated drone landing site detection",
abstract = "This paper presents an embedded video subsystem used to classify the terrain, based on an image from a camera located under the drone, for the purpose of an automatic landing system. Colour and texture features, as well as decision trees and support vector machine classifiers were analysed and evaluated. The algorithm was supported with a shadow detection module. It was evaluated on 100 test cases and achieved over 80% performance. The designed video system was implemented on two embedded platforms – a Zynq SoC (System on Chip – Field Programmable Gate Array + ARM processor system) and a Jetson GPU (Graphic Processing Unit + ARM processor system). The performance achieved on both architectures is compared and discussed.",
keywords = "Decision Trees (DT), Digital image processing, FPGA, GPU, Machine learning, Safe landing site detection, Support Vector Machine (SVM), Unmanned Aerial Vehicle (UAV), Zynq",
author = "Patryk Fraczek and Andre Mora and Tomasz Kryjak",
note = "The work presented in this paper was partially supported by the National Science Centre project no. 2016/23/D/ST6/01389 and Funda{\c c}{\~a}o para a Ciencia e a Tecnologia under the grant SFRH/BSAB/135037/2017.; International Conference on Computer Vision and Graphics, ICCVG 2018 ; Conference date: 17-09-2018 Through 19-09-2018",
year = "2018",
doi = "10.1007/978-3-030-00692-1_35",
language = "English",
isbn = "978-3-030-00691-4",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "397--409",
editor = "L. Chmielewski and R. Kozera and A. Or{\l}owski and K. Wojciechowski and A. Bruckstein and Petkov, {N. }",
booktitle = "Computer Vision and Graphics - International Conference, ICCVG 2018, 2018, Proceedings",
address = "Germany",
}