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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) pp 237–244Cite as

Counting Livestock with Image Segmentation Neural Network

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

Livestock farming industries, as well as almost any industry, want more and more data about the operation of their business and activities in order to make the right decisions. However, especially when considering very large animal farms, the precise and up-to-date information about the position and numbers of the animals is rather difficult to obtain. In this contribution, a novel engineering approach to livestock positioning and counting, based on image processing, is proposed. The approach is composed of two parts. Namely, a fully convolutional neural network for input image transformation, and a locator for animal positioning. The transformation process is designed in order to transform the original RGB image into a gray-scale image, where animal positions are highlighted as gradient circles. The locator then detects the positions of the circles in order to provide the positions of animals. The presented approach provides a precision rate of 0.9842 and a recall rate of 0.9911 with the testing set, which is, in combination with a rather suitable computational complexity, a good premise for the future implementation under real conditions.

The work has been supported by SGS grant at Faculty of Electrical Engineering and Informatics, University of Pardubice, Czech Republic. This support is very gratefully acknowledged.

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Authors and Affiliations

  1. University of Pardubice, Studentska 95, 53210, Pardubice, Czech Republic

    Petr Dolezel, Dominik Stursa, Daniel Honc, Jan Merta, Veronika Rozsivalova, Ladislav Beran & Ivo Hora

Authors
  1. Petr Dolezel
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  2. Dominik Stursa
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  3. Daniel Honc
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  4. Jan Merta
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  5. Veronika Rozsivalova
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  6. Ladislav Beran
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  7. Ivo Hora
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Corresponding author

Correspondence to Petr Dolezel .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Dolezel, P. et al. (2021). Counting Livestock with Image Segmentation Neural Network. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_23

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