Postural behavior recognition of captive nocturnal animals based on deep learning: a case study of Bengal slow loris.

Journal: Scientific reports
PMID: 35545645

Abstract

The precise identification of postural behavior plays a crucial role in evaluation of animal welfare and captive management. Deep learning technology has been widely used in automatic behavior recognition of wild and domestic fauna species. The Asian slow loris is a group of small, nocturnal primates with a distinctive locomotion mode, and a large number of individuals were confiscated into captive settings due to illegal trade, making the species an ideal as a model for postural behavior monitoring. Captive animals may suffer from being housed in an inappropriate environment and may display abnormal behavior patterns. Traditional data collection methods are time-consuming and laborious, impeding efforts to improve lorises' captive welfare and to develop effective reintroduction strategies. This study established the first human-labeled postural behavior dataset of slow lorises and used deep learning technology to recognize postural behavior based on object detection and semantic segmentation. The precision of the classification based on YOLOv5 reached 95.1%. The Dilated Residual Networks (DRN) feature extraction network showed the best performance in semantic segmentation, and the classification accuracy reached 95.2%. The results imply that computer automatic identification of postural behavior may offer advantages in assessing animal activity and can be applied to other nocturnal taxa.

Authors

  • Yujie Lei
    College of Information Engineering, Sichuan Agricultural University, Yaan, 625014, China.
  • Pengmei Dong
    Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China.
  • Yan Guan
    Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Ying Xiang
    College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China.
  • Meng Xie
    College of Life Science, Sichuan Agricultural University, Yaan, 625014, China.
  • Jiong Mu
    College of Information Engineering, Sichuan Agricultural University, Yaan, 625014, China. jmu@sicau.edu.cn.
  • Yongzhao Wang
    College of Information Engineering, Sichuan Agricultural University, Yaan, 625014, China.
  • Qingyong Ni
    Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130, China. niqy@sicau.edu.cn.

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