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J. Imaging 2017, 3(1), 4; doi:10.3390/jimaging3010004

Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing

Department of Physics and Engineering, Northwest Nazarene University, Nampa, ID 83686, USA
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Academic Editor: Gonzalo Pajares Martinsanz
Received: 1 October 2016 / Revised: 3 January 2017 / Accepted: 4 January 2017 / Published: 11 January 2017
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Abstract

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the development of an image processing approach to compare two popular off-the-shelf sUAVs: 3DR Iris+ and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every quarter of a second for two minutes. For the image processing evaluation, the position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. The hovering test results show that the 3DR Iris+ had a maximum position deviation of 0.64 m (0.126 m root mean square RMS displacement) while the DJI Phantom 2 had a maximum deviation of 0.79 m (0.150 m RMS displacement). In the rectilinear motion test, the maximum displacement for the 3DR Iris+ and the DJI phantom 2 were 0.85 m (0.134 m RMS displacement) and 0.73 m (0.372 m RMS displacement). These results demonstrated that the two sUAVs performed well in both the hovering test and the rectilinear motion test and thus demonstrated that both sUAVs can be used for civilian applications such as agricultural monitoring. The results also showed that the developed image processing approach can be used to evaluate performance of a sUAV and has the potential to be used as another feedback control parameter for autonomous navigation. View Full-Text
Keywords: digital image processing; machine vision; unmanned aerial vehicle (UAV) digital image processing; machine vision; unmanned aerial vehicle (UAV)
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Cano, E.; Horton, R.; Liljegren, C.; Bulanon, D.M. Comparison of Small Unmanned Aerial Vehicles Performance Using Image Processing. J. Imaging 2017, 3, 4.

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