期刊
FRONTIERS IN PHYSICS
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fphy.2022.1047077
关键词
broiler; respiration rate; computer vision; semantic segmentation; Euler video magnification
This study proposes a method using deep learning and machine vision to estimate the respiratory rate of broilers by analyzing the belly fluctuation signal in video data. The experiments show that this method has high accuracy and can monitor the health status of broilers contactlessly and stress-freely.
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.
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