An automatic zone detection system for safe landing of UAVs
Document Type
Article
Publication Date
1-1-2019
Abstract
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.
Keywords
UAV, disaster management, Safe landing zone, Gabor transform, Markov chain code, Safe landing detection
Divisions
fsktm
Funders
Faculty of Computer Science and Information Technology, University of Malaya: Post Graduate Funds for the RP036B-15AET and PG063-2016 A project,Natural Science Foundation of China under Grant 61672273 and Grant 61832008,Science Foundation for Distinguished Young Scholars of Jiangsu under Grant BK20160021
Publication Title
Expert Systems with Applications
Volume
122
Publisher
Elsevier