Resting Posture Recognition Method for Suckling Piglets Based on Piglet Posture Recognition (PPR)–You Only Look Once
The resting postures of piglets are crucial indicators for assessing their health status and environmental comfort. This study proposes a resting posture recognition method for piglets during lactation based on the PPR-YOLO model, aiming to enhance the detection accuracy and classification capabilit...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/3/230 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The resting postures of piglets are crucial indicators for assessing their health status and environmental comfort. This study proposes a resting posture recognition method for piglets during lactation based on the PPR-YOLO model, aiming to enhance the detection accuracy and classification capability for different piglet resting postures. Firstly, to address the issue of numerous sows and piglets in the farrowing house that easily occlude each other, an image edge detection algorithm is employed to precisely locate the sow’s farrowing bed area. By cropping the images, irrelevant background interference is reduced, thereby enhancing the model’s recognition accuracy. Secondly, to overcome the limitations of the YOLOv11 model in fine feature extraction and small object detection, improvements are made, resulting in the proposed PPR-YOLO model. Specific enhancements include the introduction of a multi-branch Conv2 module to enrich feature extraction capabilities and the adoption of an inverted bottleneck IBCNeck module, which expands the number of channels and incorporates a channel attention mechanism. This strengthens the model’s ability to capture and differentiate subtle posture features. Additionally, in the post-processing stage, the relative positions between sows and piglets are utilized to filter out piglets located outside the sow region, eliminating interference from sow nursing behaviors in resting posture recognition, thereby ensuring the accuracy of posture classification. The experimental results show that the proposed method achieves accurate piglet posture recognition, outperforming mainstream object detection algorithms. Ablation experiments validate the effectiveness of image cropping and model enhancements in improving performance. This method provides effective technical support for the automated monitoring of piglet welfare in commercial farms and holds promising application prospects. |
|---|---|
| ISSN: | 2077-0472 |