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Research Article | Open Access

Livestock detection in aerial images using a fully convolutional network

Department of Computer Technology and Application, Qinghai University, Xining, China.
Department of Computer Science and Technology, Tsinghua University, Beijing, China.
School of Computer Science and Informatics, CardiffUniversity, Cardiff, Wales, UK.
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Abstract

In order to accurately count the number of animals grazing on grassland, we present a livestock detection algorithm using modified versions of U-net and Google Inception-v4 net. This method works wellto detect dense and touching instances. We also introduce a dataset for livestock detection in aerial images, consisting of 89 aerial images collected by quadcopter. Each image has resolution of about 3000×4000 pixels, and contains livestock with varying shapes, scales, and orientations.

We evaluate our method by comparison against Faster RCNN and Yolo-v3 algorithms using our aerial livestock dataset. The average precision of our method is better than Yolo-v3 and is comparable to Faster RCNN.

Keywords

livestock detectionsegmentationclassi-fication

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Computational Visual Media
Volume 5 Issue 2,
June 2019
Pages 221-228
DOI: 10.1007/s41095-019-0132-5

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Cite this article:
Han L, Tao P, Martin RR. Livestock detection in aerial images using a fully convolutional network. Computational Visual Media, 2019, 5(2): 221-228. https://doi.org/10.1007/s41095-019-0132-5

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Revised: 12 December 2018
Accepted: 27 January 2019
Published: 30 March 2019
© The author(s) 2019

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