Izvorni znanstveni članak
https://doi.org/10.24138/jcomss.v14i1.441
Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks
Dunja Božić-Štulić
orcid.org/0000-0001-5267-0848
; University of Split, Croatia
Stanko Kružić
orcid.org/0000-0003-0756-6982
; University of Split, Croatia
Sven Gotovac
; University of Split, Croatia
Vladan Papić
orcid.org/0000-0002-1873-6353
; University of Split, Croatia
Sažetak
In this paper, a novel approach for an automatic object detection and localisation on aerial images is proposed. Proposed model does not use ground control points (GCPs) and consists of three major phases. In the first phase, optimal flight route is planned in order to capture the area of interest and aerial images are acquired using unmanned aerial vehicle (UAV), followed by creating a mosaic of collected images to obtained larger field-of-view panoramic image of the area of interest and using the obtained image mosaic to create georeferenced map. The image mosaic is then also used to detect objects of interest using the approach based on convolutional neural networks.
Ključne riječi
georeferencing; GIS; UAV; image mosaic; object detection; convoloutional neural networks
Hrčak ID:
199206
URI
Datum izdavanja:
15.3.2018.
Posjeta: 1.764 *