UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods

Marcin Paszkuta; Jakub Rosner; Damian Pęszor; Marcin Szender; Marzena Wojciechowska; Konrad Wojciechowski; Jerzy Paweł Nowacki

doi:10.1007/978-3-030-73280-6_37

UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods

Marcin Paszkuta
, Jakub Rosner
, Damian Pęszor
, Marcin Szender
, Marzena Wojciechowska
, Konrad Wojciechowski
, Jerzy Paweł Nowacki

Department of Graphics, Computer Vision and Digital Systems (RAU/RAU6)

Polish-Japanese Academy of Information Technology
MSP InnTech sp. z o.o.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

This article proposes the system designed for automatic detection of emergency landing sites for horizontally landing unmanned aerial vehicles (UAV). The presented solution combines the classic computer vision algorithms and novel segmentation methods based on deep learning techniques using the U-Net inspired network architecture. The presented system uses a single nadir camera mounted on a UAV and the energy-efficient compute module capable of highly-parallelized calculations.

Original language	English
Title of host publication	Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings
Editors	Ngoc Thanh Nguyen, Suphamit Chittayasothorn, Dusit Niyato, Bogdan Trawiński
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	467-478
Number of pages	12
ISBN (Print)	9783030732790
DOIs	https://doi.org/10.1007/978-3-030-73280-6_37
Publication status	Published - 2021
Event	13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021 - Phuket, Thailand Duration: 7 Apr 2021 → 10 Apr 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12672 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021
Country/Territory	Thailand
City	Phuket
Period	7/04/21 → 10/04/21

Keywords

3D simulation
AirSim
Convolutional neural network
Landing sites detection
Semantic segmentation
U-Net
UAV
Unmanned aerial vehicle
Unreal engine

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-73280-6_37

Cite this

Paszkuta, M., Rosner, J., Pęszor, D., Szender, M., Wojciechowska, M., Wojciechowski, K., & Nowacki, J. P. (2021). UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods. In N. T. Nguyen, S. Chittayasothorn, D. Niyato, & B. Trawiński (Eds.), Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings (pp. 467-478). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12672 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73280-6_37

Paszkuta, Marcin ; Rosner, Jakub ; Pęszor, Damian et al. / UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods. Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings. editor / Ngoc Thanh Nguyen ; Suphamit Chittayasothorn ; Dusit Niyato ; Bogdan Trawiński. Springer Science and Business Media Deutschland GmbH, 2021. pp. 467-478 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods",

abstract = "This article proposes the system designed for automatic detection of emergency landing sites for horizontally landing unmanned aerial vehicles (UAV). The presented solution combines the classic computer vision algorithms and novel segmentation methods based on deep learning techniques using the U-Net inspired network architecture. The presented system uses a single nadir camera mounted on a UAV and the energy-efficient compute module capable of highly-parallelized calculations.",

keywords = "3D simulation, AirSim, Convolutional neural network, Landing sites detection, Semantic segmentation, U-Net, UAV, Unmanned aerial vehicle, Unreal engine",

author = "Marcin Paszkuta and Jakub Rosner and Damian P{\c e}szor and Marcin Szender and Marzena Wojciechowska and Konrad Wojciechowski and Nowacki, \{Jerzy Pawe{\l}\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021 ; Conference date: 07-04-2021 Through 10-04-2021",

year = "2021",

doi = "10.1007/978-3-030-73280-6\_37",

language = "English",

isbn = "9783030732790",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "467--478",

editor = "Nguyen, \{Ngoc Thanh\} and Suphamit Chittayasothorn and Dusit Niyato and Bogdan Trawi{\'n}ski",

booktitle = "Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings",

address = "Germany",

}

Paszkuta, M, Rosner, J, Pęszor, D, Szender, M, Wojciechowska, M, Wojciechowski, K & Nowacki, JP 2021, UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods. in NT Nguyen, S Chittayasothorn, D Niyato & B Trawiński (eds), Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12672 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 467-478, 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021, Phuket, Thailand, 7/04/21. https://doi.org/10.1007/978-3-030-73280-6_37

UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods. / Paszkuta, Marcin; Rosner, Jakub; Pęszor, Damian et al.
Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings. ed. / Ngoc Thanh Nguyen; Suphamit Chittayasothorn; Dusit Niyato; Bogdan Trawiński. Springer Science and Business Media Deutschland GmbH, 2021. p. 467-478 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12672 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods

AU - Paszkuta, Marcin

AU - Rosner, Jakub

AU - Pęszor, Damian

AU - Szender, Marcin

AU - Wojciechowska, Marzena

AU - Wojciechowski, Konrad

AU - Nowacki, Jerzy Paweł

PY - 2021

Y1 - 2021

N2 - This article proposes the system designed for automatic detection of emergency landing sites for horizontally landing unmanned aerial vehicles (UAV). The presented solution combines the classic computer vision algorithms and novel segmentation methods based on deep learning techniques using the U-Net inspired network architecture. The presented system uses a single nadir camera mounted on a UAV and the energy-efficient compute module capable of highly-parallelized calculations.

AB - This article proposes the system designed for automatic detection of emergency landing sites for horizontally landing unmanned aerial vehicles (UAV). The presented solution combines the classic computer vision algorithms and novel segmentation methods based on deep learning techniques using the U-Net inspired network architecture. The presented system uses a single nadir camera mounted on a UAV and the energy-efficient compute module capable of highly-parallelized calculations.

KW - 3D simulation

KW - AirSim

KW - Convolutional neural network

KW - Landing sites detection

KW - Semantic segmentation

KW - U-Net

KW - UAV

KW - Unmanned aerial vehicle

KW - Unreal engine

UR - https://www.scopus.com/pages/publications/85104727849

U2 - 10.1007/978-3-030-73280-6_37

DO - 10.1007/978-3-030-73280-6_37

M3 - Conference contribution

AN - SCOPUS:85104727849

SN - 9783030732790

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 467

EP - 478

BT - Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings

A2 - Nguyen, Ngoc Thanh

A2 - Chittayasothorn, Suphamit

A2 - Niyato, Dusit

A2 - Trawiński, Bogdan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021

Y2 - 7 April 2021 through 10 April 2021

ER -

Paszkuta M, Rosner J, Pęszor D, Szender M, Wojciechowska M, Wojciechowski K et al. UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods. In Nguyen NT, Chittayasothorn S, Niyato D, Trawiński B, editors, Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 467-478. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-73280-6_37

UAV On-Board Emergency Safe Landing Spot Detection System Combining Classical and Deep Learning-Based Segmentation Methods

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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