Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks
Abstract
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.
Keywords
georeferencing, GIS, UAV, image mosaic, object detection, convoloutional neural networks
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
D. Božić-Štulić, S. Kružić, S. Gotovac and V. Papić, "Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks," in Journal of Communications Software and Systems, vol. 14, no. 1, pp. 82-90, March 2018, doi: 10.24138/jcomss.v14i1.441
@article{bozic-stulic2018completemodel,
author = {Dunja Božić-Štulić and Stanko Kružić and Sven Gotovac and Vladan Papić},
title = {Complete Model for Automatic Object Detection and Localisation on Aerial Images using Convolutional Neural Networks},
journal = {Journal of Communications Software and Systems},
month = {3},
year = {2018},
volume = {14},
number = {1},
pages = {82--90},
doi = {10.24138/jcomss.v14i1.441},
url = {https://doi.org/10.24138/jcomss.v14i1.441}
}