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Updated: Jul 11, 2025
04:48
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
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Pig Counting Algorithm Based on Improved YOLOv5n Model with Multiscene and Fewer Number of Parameters
Yongsheng Wang1,2, Duanli Yang1,2, Hui Chen3,4
1College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China.
Animals : an Open Access Journal From MDPI
|November 14, 2023
Summary
An improved YOLOv5n algorithm enhances pig counting accuracy in complex farm environments. This optimized model significantly reduces parameters and computational load, enabling practical Android application development for livestock monitoring.
Area of Science:
- Computer Vision
- Machine Learning
- Agricultural Technology
Background:
- Accurate pig counting is crucial for large-scale pig farm management.
- Existing methods struggle with challenges like occlusion, varying illumination, and diverse imaging conditions.
Purpose of the Study:
- To develop a high-precision pig counting algorithm with reduced model complexity.
- To create a practical pig counting application for Android systems.
Main Methods:
- A multi-scene dataset was created to improve model generalization.
- YOLOv5n's backbone was replaced with FasterNet for parameter reduction.
- The Neck was optimized using E-GFPN for enhanced feature fusion.
- Focal EIoU loss function was implemented to boost identification accuracy.
Main Results:
- The improved model achieved an AP of 97.72%.
- Parameters, computation, and model size were reduced by 50.57%, 32.20%, and 47.21% respectively, compared to YOLOv5n.
- Detection speed reached 75.87 f/s, with enhanced accuracy and robustness in complex environments.
Conclusions:
- The optimized YOLOv5n algorithm offers superior accuracy and efficiency for pig counting.
- A functional Android application was developed, demonstrating practical utility.
- The approach is extensible to other livestock counting applications, showing broad practical value.
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