Article
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Cattle Detection Using Oblique UAV Images
Version 1
: Received: 10 November 2020 / Approved: 12 November 2020 / Online: 12 November 2020 (08:57:18 CET)
A peer-reviewed article of this Preprint also exists.
Barbedo, J.G.A.; Koenigkan, L.V.; Santos, P.M. Cattle Detection Using Oblique UAV Images. Drones 2020, 4, 75. Barbedo, J.G.A.; Koenigkan, L.V.; Santos, P.M. Cattle Detection Using Oblique UAV Images. Drones 2020, 4, 75.
Abstract
The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for the experiments, which covered aspects like ideal input dimensions, effect of the distance between animals and sensor, effect of classification error on the overall detection process, and impact of physical obstacles on the accuracy of the model. Experimental results indicate that oblique images can be successfully used under certain conditions, but some practical limitations need to be addressed in order to make this approach appealing.
Keywords
Convolutional neural network; Unmanned Aerial Vehicles; Deep learning.
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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