The estimation of broiler respiration rate based on the semantic segmentation and video amplification.
Articolo
Data di Pubblicazione:
2022
Citazione:
The estimation of broiler respiration rate based on the semantic segmentation and video amplification / J. Wang, L. Liu, M. Lu, C. Okinda, D. Lovarelli, M. Guarino, M. Shen. - In: FRONTIERS IN PHYSICS. - ISSN 2296-424X. - 10:(2022 Dec), pp. 1-13. [10.3389/fphy.2022.1047077]
Abstract:
Respiratory rate is an indicator of a broilers’ stress and health status, thus, it is
essential to detect respiratory rate contactless and stress-freely. This study
proposed an estimation method of broiler respiratory rate by deep learning and
machine vision. Experiments were performed at New Hope (Shandong
Province, P. R. China) and Wen’s group (Guangdong Province, P. R. China),
and a total of 300 min of video data were collected. By separating video frames,
a data set of 3,000 images was made, and two semantic segmentation models
were trained. The single-channel Euler video magnification algorithm was used
to amplify the belly fluctuation of the broiler, which saved 55% operation time
compared with the traditional Eulerian video magnification algorithm. The
contour features significantly related to respiration were used to obtain the
signals that could estimate broilers’ respiratory rate. Detrending and band-pass
filtering eliminated the influence of broiler posture conversion and motion on
the signal. The mean absolute error, root mean square error, average accuracy
of the proposed respiratory rate estimation technique for broilers were 3.72%,
16.92%, and 92.19%, respectively.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
broiler; respiration rate; computer vision; semantic segmentation; Euler video magnification
Elenco autori:
J. Wang, L. Liu, M. Lu, C. Okinda, D. Lovarelli, M. Guarino, M. Shen
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