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ThinkMind // DATA ANALYTICS 2022, The Eleventh International Conference on Data Analytics // View article data_analytics_2022_2_40_60037


Using Deep Learning for Automated Tail Posture Detection of Pigs

Authors:
Jan-Hendrik Witte
Johann Gerberding
Jorge Marx Gómez

Keywords: recision livestock farming; tail biting; tail posture; deep learning; computer vision

Abstract:
Tail biting is one of the biggest problems in pig livestock farming. One indicator that can be observed before an outbreak is the change in tail posture. Studies have shown that days before a tail biting outbreak, a steady increase in hanging tail postures can be observed. A continuous monitoring of this indicator could therefore be used to inform farmers of potential problems arising within respective pens. This paper therefore presents a first step in the development of automated monitoring systems for early detection of tail biting indicators by evaluating different approaches for tail posture detection using image data and Deep Learning. Using a dataset consisting of 1000 annotated images, different YOLOv5 object detection models were trained to detect upright and hanging tail postures. The results show that there are significant differences in performance for the detection of upright and hanging class. To further investigate the problem, an EfficientNetv2 image classification model was trained to examine if similar performance differences for the two classes could be observed. Considered in isolation, these differences could be mitigated. However, potentials could not be utilized, as the results of the comparison of the one-step detection of tail posture using YOLOv5 and the introduced two-step detection using YOLOv5 for tail detection and EfficientNetv2 for tail posture classification shows. Based on the discussion of the possible explanations for the inferior performance as well as the summary of the key findings of this paper, we present approaches that can be used as a basis for future research.

Pages: 33 to 42

Copyright: Copyright (c) IARIA, 2022

Publication date: November 13, 2022

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-994-2

Location: Valencia, Spain

Dates: from November 13, 2022 to November 17, 2022

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