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An automatic zone detection system for safe landing of UAVs

As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting region...

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Main Authors:	Kaljahi, Maryam Asadzadeh, Shivakumara, Palaiahnakote, Idris, Mohd Yamani Idna, Anisi, Mohammad Hossein, Lu, Tong, Blumenstein, Michael, Noor, Noorzaily Mohamed
Format:	Article
Published:	Elsevier 2019
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://eprints.um.edu.my/20060/ https://doi.org/10.1016/j.eswa.2019.01.024
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id	my.um.eprints.20060
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spelling	my.um.eprints.200602019-01-18T01:46:15Z http://eprints.um.edu.my/20060/ An automatic zone detection system for safe landing of UAVs Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Idris, Mohd Yamani Idna Anisi, Mohammad Hossein Lu, Tong Blumenstein, Michael Noor, Noorzaily Mohamed QA75 Electronic computers. Computer science QA76 Computer software As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems. Elsevier 2019 Article PeerReviewed Kaljahi, Maryam Asadzadeh and Shivakumara, Palaiahnakote and Idris, Mohd Yamani Idna and Anisi, Mohammad Hossein and Lu, Tong and Blumenstein, Michael and Noor, Noorzaily Mohamed (2019) An automatic zone detection system for safe landing of UAVs. Expert Systems with Applications, 122. pp. 319-333. ISSN 0957-4174 https://doi.org/10.1016/j.eswa.2019.01.024 doi:10.1016/j.eswa.2019.01.024
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Idris, Mohd Yamani Idna Anisi, Mohammad Hossein Lu, Tong Blumenstein, Michael Noor, Noorzaily Mohamed An automatic zone detection system for safe landing of UAVs
description	As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems.
format	Article
author	Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Idris, Mohd Yamani Idna Anisi, Mohammad Hossein Lu, Tong Blumenstein, Michael Noor, Noorzaily Mohamed
author_facet	Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Idris, Mohd Yamani Idna Anisi, Mohammad Hossein Lu, Tong Blumenstein, Michael Noor, Noorzaily Mohamed
author_sort	Kaljahi, Maryam Asadzadeh
title	An automatic zone detection system for safe landing of UAVs
title_short	An automatic zone detection system for safe landing of UAVs
title_full	An automatic zone detection system for safe landing of UAVs
title_fullStr	An automatic zone detection system for safe landing of UAVs
title_full_unstemmed	An automatic zone detection system for safe landing of UAVs
title_sort	automatic zone detection system for safe landing of uavs
publisher	Elsevier
publishDate	2019
url	http://eprints.um.edu.my/20060/ https://doi.org/10.1016/j.eswa.2019.01.024
_version_	1643691167375687680
score	13.211869

An automatic zone detection system for safe landing of UAVs

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