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Livestock detection in aerial images using a fully convolutional network

  • Liang HanEmail author
  • Pin Tao
  • Ralph R. Martin
Open Access
Research Article
  • 66 Downloads

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 well to 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 detection segmentation classification 

Notes

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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Authors and Affiliations

  1. 1.Department of Computer Technology and ApplicationQinghai UniversityXiningChina
  2. 2.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.School of Computer Science and InformaticsCardiff UniversityCardiff, WalesUK

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