Authors
Keiji Kamei1, *, Masahiro Kaneoka2, Ken Yanai2, Masaya Umemoto2, Hiroki Yamaguchi2, Kazuki Osawa2
1Department of Production Systems, Graduate School of Engineering, Nishinippon Institute of Technology, 1-11 Aratsu, Kanda, Miyakogun, Fukuoka 800-0394, Japan
2Department of Information Systems, School of Engineering, Nishinippon Institute of Technology, 1-11 Aratsu, Kanda, Miyakogun, Fukuoka 800-0394, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Keiji Kamei
Received 2 December 2019, Accepted 8 April 2020, Available Online 2 June 2020.
DOI
https://doi.org/10.2991/jrnal.k.200528.003How to use a DOI?
Keywords
Deep learning; region convolutional neural networks; structure from motion; simultaneous localization and mapping; recognition of trees; drone in a forest
Abstract
Drones have been used in many purposes for a long time. Especially, development of the automatic observation systems such as measurement using drones for the primary sector of industry have been frequently researched in recent years. The measurement of a tree growth in a forest is also one of the aim for a drone application. In this study, our aim is to develop the automatic measurement system for size of a tree in a forest. The difficulties are that a drone has to recognize trees, to construct a map of a forest and to measure the size of trees from a front camera. To overcome those difficulties, we propose that a drone recognizes trees based on single shot multibox detector (SSD), constructs a map from simultaneous localization and mapping (SLAM) and measures a tree by structure from motion (SfM). Experimental results from the drone competition show that a drone has been able to recognize a tree and to fly safety.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).