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Computational Visual Media 2019, 5(2): 221-228 https://doi.org/10.1007/s41095-019-0132-5
Research Article | Open Access | Issue | Published: 30 March 2019

Livestock detection in aerial images using a fully convolutional network

Show Author's Information Liang Han1( ),  Pin Tao2,  Ralph R. Martin3
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.
Keywords:
segmentation, livestock detection, classi-fication
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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Abstract Full text About this article

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.


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About this article

Livestock detection in aerial images using a fully convolutional network

Show Author's information Liang Han1( ), Pin Tao2, Ralph R. Martin3
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.

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: segmentation, livestock detection, classi-fication

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Publication history
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Publication history

Revised: 12 December 2018
Accepted: 27 January 2019
Published: 30 March 2019
Issue date: June 2019

Copyright

© The author(s) 2019

Acknowledgements

This work was supported by the Scientific and Technological Achievements Transformation Project of Qinghai, China (Project No. 2018-SF-110), and the National Natural Science Foundation of China (Projects Nos. 61866031 and 61862053).

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