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

文献类型： 外文期刊

第一作者： Zha, Wenwen

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

作者机构：

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.

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