College of Information Engineering, Sichuan Agricultural University, Yaan, 625014, China.
Sichuan Key Laboratory of Agricultural Information Engineering, Yaan, 625000, China.
Sci Rep. 2022 May 11;12(1):7738. doi: 10.1038/s41598-022-11842-0.
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.
姿势行为的准确识别在评估动物福利和圈养管理方面起着至关重要的作用。深度学习技术已广泛应用于野生动物和家畜物种的自动行为识别。亚洲懒猴是一组小型、夜行性灵长类动物，具有独特的运动模式，由于非法贸易，大量个体被没收进入圈养环境，使该物种成为监测姿势行为的理想模型。圈养动物可能因生活在不合适的环境中而遭受痛苦，并且可能表现出异常的行为模式。传统的数据收集方法既耗时又费力，阻碍了提高懒猴圈养福利和制定有效重新引入策略的努力。本研究建立了第一个慢猴人类标记姿势行为数据集，并使用深度学习技术基于目标检测和语义分割来识别姿势行为。基于 YOLOv5 的分类精度达到 95.1%。空洞残差网络（DRN）特征提取网络在语义分割中表现出最佳性能，分类准确率达到 95.2%。结果表明，计算机自动识别姿势行为可能在评估动物活动方面具有优势，并且可应用于其他夜行物种。
