Livestock detection in aerial images using a fully convolutional network

Liang Han; Pin Tao; Ralph R. Martin

doi:10.1007/s41095-019-0132-5

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Research Article | Open Access

Livestock detection in aerial images using a fully convolutional network

Liang Han^¹(

), Pin Tao^², Ralph R. Martin^³

1 Department of Computer Technology and Application, Qinghai University, Xining, China.

2 Department of Computer Science and Technology, Tsinghua University, Beijing, China.

3 School of Computer Science and Informatics, CardiffUniversity, Cardiff, Wales, UK.

Show Author Information

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 $3000 \times 4000$ 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 detection segmentation classi-fication

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Computational Visual Media

Volume 5 Issue 2,
June 2019

Pages 221-228

DOI: 10.1007/s41095-019-0132-5

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

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