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Multimodel Deep Learning for Person Detection in Aerial Images (CROSBI ID 282722)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Mirela, Kundid Vasić ; Papić, Vladan Multimodel Deep Learning for Person Detection in Aerial Images // Electronics (Basel), 9(9) (2020), 1459; 01459, 15. doi: 10.3390/electronics9091459

Podaci o odgovornosti

Mirela, Kundid Vasić ; Papić, Vladan

engleski

Multimodel Deep Learning for Person Detection in Aerial Images

In this paper, we propose a novel method for person detection in aerial images of nonurban terrain gathered by an Unmanned Aerial Vehicle (UAV), which plays an important role in Search And Rescue (SAR) missions. The UAV in SAR operations contributes significantly due to the ability to survey a larger geographical area from an aerial viewpoint. Because of the high altitude of recording, the object of interest (person) covers a small part of an image (around 0.1%), which makes this task quite challenging. To address this problem, a multimodel deep learning approach is proposed. The solution consists of two different convolutional neural networks in region proposal, as well as in the classification stage. Additionally, contextual information is used in the classification stage in order to improve the detection results. Experimental results tested on the HERIDAL dataset achieved precision of 68.89% and a recall of 94.65%, which is better than current state-of-the-art methods used for person detection in similar scenarios. Consequently, it may be concluded that this approach is suitable for usage as an auxiliary method in real SAR operations.

convolutional neural networks ; aerial images ; person detection ; search and rescue

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Podaci o izdanju

9(9) (1459)

2020.

01459

15

objavljeno

2079-9292

10.3390/electronics9091459

Trošak objave rada u otvorenom pristupu

Povezanost rada

Elektrotehnika, Računarstvo

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