ArticleSensors (Basel, Switzerland)2023
Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Computer Vision Applied to the Analysis of Pig Behavior Patterns in an Air-Conditioned Environment.Animals : an open access journal from MDPI · 2026Article
- Hybrid neural network for personnel recognition and tracking in remote bidding evaluation monitoring.Scientific reports · 2026Article
- Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Technical Aspects of Deploying UAV and Ground Robots for Intelligent Logistics Using YOLO on Embedded Systems.Sensors (Basel, Switzerland) · 2025Article
- Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.