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Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing.
- Source :
-
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Nov 03; Vol. 23 (21). Date of Electronic Publication: 2023 Nov 03. - Publication Year :
- 2023
-
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.
- Subjects :
- Swine
Animals
Farms
Posture
Recognition, Psychology
Sus scrofa
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 1424-8220
- Volume :
- 23
- Issue :
- 21
- Database :
- MEDLINE
- Journal :
- Sensors (Basel, Switzerland)
- Publication Type :
- Academic Journal
- Accession number :
- 37960652
- Full Text :
- https://doi.org/10.3390/s23218952