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

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 identificati...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-11, Vol.23 (21), p.8952
Main Authors: Zha, Wenwen, Li, Hualong, Wu, Guodong, Zhang, Liping, Pan, Weihao, Gu, Lichuan, Jiao, Jun, Zhang, Qiang
Format: Article
Language:English
Subjects:
Algorithms
Animal welfare
behavior monitoring
Cameras
Datasets
Deep learning
edge computing
Energy consumption
Foraging behavior
Hogs
multi-target tracking
Sensors
Swine
target detection
Target recognition
Telecommunication systems
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23218952