您好，欢迎访问安徽省农业科学院 机构知识库！

Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing

文献类型： 外文期刊

作者： Zha, Wenwen 1 ; Li, Hualong 2 ; Wu, Guodong 1 ; Zhang, Liping 3 ; Pan, Weihao 1 ; Gu, Lichuan 1 ; Jiao, Jun 1 ; Zhang, Qiang 4 ;

作者机构： 1.Anhui Agr Univ, Sch Informat & Comp, Hefei 230036, Peoples R China

2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China

3.Anhui Acad Agr Sci, Inst Agr Econ & Informat, Hefei 230031, Peoples R China

4.Univ Manitoba, Dept Biosyst Engn, Winnipeg, MB R3T 5V6, Canada

关键词： behavior monitoring; target detection; multi-target tracking; edge computing

期刊名称：SENSORS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN：

年卷期： 2023 年 23 卷 21 期

页码：

收录情况： SCI

摘要： The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：安徽省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台