A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Pose estimation of sow and piglets during free farrowing using deep learning
Tekijät: Farahnakian Fahimeh, Farahnakian Farshad, Björkman Stefan, Bloch Victor, Pastell Matti, Heikkonen Jukka
Kustantaja: Elsevier
Julkaisuvuosi: 2024
Journal: Journal of agriculture and food research
Tietokannassa oleva lehden nimi: Journal of Agriculture and Food Research
Artikkelin numero: 101067
Vuosikerta: 16
ISSN: 2666-1543
eISSN: 2666-1543
DOI: https://doi.org/10.1016/j.jafr.2024.101067
Verkko-osoite: https://doi.org/10.1016/j.jafr.2024.101067
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/387398303
Automatic and real-time pose estimation is important in monitoring animal behavior, health, and welfare. In this paper, we utilized pose estimation for monitoring the farrowing process to prevent piglet mortality and preserve the health and welfare of the sow. State-of-the-art Deep Learning (DL) methods have lately been used for animal pose estimation. This paper aims to probe the generalization ability of five common DL networks (ResNet50, ResNet101, MobileNet, EfficientNet, and DLCRNet) for sow and piglet pose estimation. These architectures predict the body parts of several piglets and the sow directly from input video sequences. Real farrowing data from a commercial farm was used for training and validation of the proposed networks. The experimental results demonstrated that MobileNet was able to detect seven body parts of the sow with a median test error of 0.61 pixels.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
