Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets
In modern intensive pig farming, accurately monitoring piglet behavior is crucial for health management and improving production efficiency. However, the complexity of existing models demands high computational resources, limiting the application of piglet behavior recognition in farming environment...
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MDPI AG
2025-05-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/15/11/1563 |
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| author | Yizhi Luo Kai Lin Zixuan Xiao Yuankai Chen Chen Yang Deqin Xiao |
| author_facet | Yizhi Luo Kai Lin Zixuan Xiao Yuankai Chen Chen Yang Deqin Xiao |
| author_sort | Yizhi Luo |
| collection | DOAJ |
| description | In modern intensive pig farming, accurately monitoring piglet behavior is crucial for health management and improving production efficiency. However, the complexity of existing models demands high computational resources, limiting the application of piglet behavior recognition in farming environments. In this study, the piglet multi-behavior-recognition approach is divided into three stages. In the first stage, the LAMP pruning algorithm is used to prune and optimize redundant channels, resulting in the lightweight YOLOv8-Prune. In the second stage, based on YOLOv8, the AIFI module and the Gather–Distribute mechanism are incorporated, resulting in YOLOv8-GDA. In the third stage, using YOLOv8-GDA as the teacher model and YOLOv8-Prune as the student model, knowledge distillation is employed to further enhance detection accuracy, thus obtaining the YOLOv8-Piglet model, which strikes a balance between the detection accuracy and speed. Compared to the baseline model, YOLOv8-Piglet significantly reduces model complexity while improving detection performance, with a 6.3% increase in precision, 11.2% increase in recall, and an mAP@0.5 of 91.8%. The model was deployed on the NVIDIA Jetson Orin NX edge computing platform for the evaluation. The average inference time was reduced from 353.9 ms to 163.2 ms, resulting in a 53.8% reduction in the processing time. This study achieves a balance between model compression and recognition accuracy through the collaborative optimization of pruning and knowledge extraction. |
| format | Article |
| id | doaj-art-386bafbcfe33423aa2d970b975b02583 |
| institution | OA Journals |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Animals |
| spelling | doaj-art-386bafbcfe33423aa2d970b975b025832025-08-20T02:23:00ZengMDPI AGAnimals2076-26152025-05-011511156310.3390/ani15111563Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in PigletsYizhi Luo0Kai Lin1Zixuan Xiao2Yuankai Chen3Chen Yang4Deqin Xiao5Key Laboratory of Agricultural Equipment Technology, College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaKey Laboratory of Agricultural Equipment Technology, College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaKey Laboratory of Agricultural Equipment Technology, College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaInstitute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, ChinaKey Laboratory of Agricultural Equipment Technology, College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaIn modern intensive pig farming, accurately monitoring piglet behavior is crucial for health management and improving production efficiency. However, the complexity of existing models demands high computational resources, limiting the application of piglet behavior recognition in farming environments. In this study, the piglet multi-behavior-recognition approach is divided into three stages. In the first stage, the LAMP pruning algorithm is used to prune and optimize redundant channels, resulting in the lightweight YOLOv8-Prune. In the second stage, based on YOLOv8, the AIFI module and the Gather–Distribute mechanism are incorporated, resulting in YOLOv8-GDA. In the third stage, using YOLOv8-GDA as the teacher model and YOLOv8-Prune as the student model, knowledge distillation is employed to further enhance detection accuracy, thus obtaining the YOLOv8-Piglet model, which strikes a balance between the detection accuracy and speed. Compared to the baseline model, YOLOv8-Piglet significantly reduces model complexity while improving detection performance, with a 6.3% increase in precision, 11.2% increase in recall, and an mAP@0.5 of 91.8%. The model was deployed on the NVIDIA Jetson Orin NX edge computing platform for the evaluation. The average inference time was reduced from 353.9 ms to 163.2 ms, resulting in a 53.8% reduction in the processing time. This study achieves a balance between model compression and recognition accuracy through the collaborative optimization of pruning and knowledge extraction.https://www.mdpi.com/2076-2615/15/11/1563pigletmulti-behavior recognitionprunedistillprecision livestock farming |
| spellingShingle | Yizhi Luo Kai Lin Zixuan Xiao Yuankai Chen Chen Yang Deqin Xiao Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets Animals piglet multi-behavior recognition prune distill precision livestock farming |
| title | Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets |
| title_full | Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets |
| title_fullStr | Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets |
| title_full_unstemmed | Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets |
| title_short | Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets |
| title_sort | collaborative optimization of model pruning and knowledge distillation for efficient and lightweight multi behavior recognition in piglets |
| topic | piglet multi-behavior recognition prune distill precision livestock farming |
| url | https://www.mdpi.com/2076-2615/15/11/1563 |
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