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基于集成可见光/红外热成像摄像机的计算机视觉算法和机器学习建模获取非侵入式绵羊生物特征。

Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras.

机构信息

Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia.

Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia.

出版信息

Sensors (Basel). 2020 Nov 6;20(21):6334. doi: 10.3390/s20216334.

DOI:10.3390/s20216334
PMID:33171995
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7664231/
Abstract

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting.

摘要

活羊出口已成为公众关注的焦点。本研究旨在测试一种基于人工智能的非接触式生物识别系统，以评估即将用于农场和运输中自动动物福利评估的绵羊的热应激情况。使用自动跟踪算法从红外热视频 (IRTV) 中提取头部特征的皮肤温度 (°C)。对 RGB 视频执行了两个参数工程程序，以评估每分钟心跳数 (BPM) 和每分钟呼吸数 (BrPM)：(i) 使用绿色通道 (G) 亮度的变化，和 (ii) 使用 CIELAB 颜色尺度的绿色通道到红色通道 (a) 的变化。使用原始 RR 参数作为输入，开发了一个有监督的机器学习 (ML) 分类模型，以对低、中、高呼吸率的截止频率进行分类 (模型 1)。使用模型 1 中的原始 HR 和 RR 参数开发了一个有监督的 ML 回归模型 (模型 2)。结果表明，模型 1 和 2 在 RR 频率水平的估计方面具有很高的准确性，总体准确性为 96%（模型 1），HR 和 RR 的 R = 0.94 和斜率 = 0.76（模型 2），没有过度拟合的统计迹象。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/247ead6dc152/sensors-20-06334-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/599c14ae2012/sensors-20-06334-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/8fa3bb497b80/sensors-20-06334-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/6ccd70c2d6a6/sensors-20-06334-g003a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/973dc144fd7e/sensors-20-06334-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/d53dc01059fc/sensors-20-06334-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/e70a9cecd34c/sensors-20-06334-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/5c112ac54181/sensors-20-06334-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/ce2683ee7915/sensors-20-06334-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/247ead6dc152/sensors-20-06334-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/599c14ae2012/sensors-20-06334-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/8fa3bb497b80/sensors-20-06334-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/6ccd70c2d6a6/sensors-20-06334-g003a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/973dc144fd7e/sensors-20-06334-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/d53dc01059fc/sensors-20-06334-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/e70a9cecd34c/sensors-20-06334-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/5c112ac54181/sensors-20-06334-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/ce2683ee7915/sensors-20-06334-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173b/7664231/247ead6dc152/sensors-20-06334-g009.jpg

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