@inproceedings{abdollahzadeh_safe_2022,

title = {Safe Landing Zones Detection for UAVs Using Deep Regression},

author = {S. Abdollahzadeh and P. -L. Proulx and M. S. Allili and J. -F. Lapointe},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138466098&doi=10.1109%2fCRV55824.2022.00035&partnerID=40&md5=9183f6cd002c8a9068716faf66da72ec},

doi = {10.1109/CRV55824.2022.00035},

isbn = {978-1-66549-774-9},

year  = {2022},

date = {2022-01-01},

booktitle = {Proceedings - 2022 19th Conference on Robots and Vision, CRV 2022},

pages = {213–218},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Finding safe landing zones (SLZ) in urban areas and natural scenes is one of the many challenges that must be overcome in automating Unmanned Aerial Vehicles (UAV) navigation. Using passive vision sensors to achieve this objective is a very promising avenue due to their low cost and the potential they provide for performing simultaneous terrain analysis and 3D reconstruction. In this paper, we propose using a deep learning approach on UAV imagery to assess the SLZ. The model is built on a semantic segmentation architecture whereby thematic classes of the terrain are mapped into safety scores for UAV landing. Contrary to past methods, which use hard classification into safe/unsafe landing zones, our approach provides a continuous safety map that is more practical for an emergency landing. Experiments on public datasets have shown promising results. © 2022 IEEE.},

keywords = {Aerial vehicle, Air navigation, Aircraft detection, Antennas, Automatic unmanned aerial vehicle navigation, Deep learning, Deep regression, Landing, Landing zones, Safe landing, Safe landing zone, Semantic segmentation, Semantics, Unmanned aerial vehicles (UAV), Urban areas, Vehicle navigation, Zone detection},

pubstate = {published},

tppubtype = {inproceedings}

}