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Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
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Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT
Peng Shen1, Keyu Mei1, Haori Xue1
1North China Institute of Aerospace Engineering, School of Aeronautics and Astronautics, Langfang 065000, China.
Sensors (Basel, Switzerland)
|May 14, 2025
Summary
This study introduces an improved YOLOv8n-EGV+Deep SORT-P algorithm for accurate dynamic pig counting in farms. The enhanced model significantly boosts counting accuracy and tracking stability, addressing limitations of manual methods.
Area of Science:
- Agricultural technology
- Computer vision
- Animal science
Background:
- Manual pig counting is labor-intensive and inaccurate.
- Existing automated methods struggle with detection and tracking challenges like crowding and occlusion.
Purpose of the Study:
- To develop an enhanced algorithm for accurate and stable dynamic pig counting.
- To improve pig target recognition and tracking accuracy in real-world farming environments.
Main Methods:
- Integration of ELA attention, GSConv, and VoVGSCSP modules into YOLOv8n for improved detection.
- Enhancement of Deep SORT with DenseNet and CIoU for robust tracking.
- Validation using pig videos from farm passages.
Main Results:
- The improved YOLOv8n-EGV+Deep SORT-P algorithm achieved 92.1% counting accuracy, a 17.5% improvement.
- Enhanced Deep SORT-P tracking showed improved MOTA (89.2%) and MOTP (90.4%), with reduced IDSW.
- The algorithm demonstrated stable dynamic pig counting in practical settings.
Conclusions:
- The proposed algorithm offers a significant advancement over existing methods for dynamic pig counting.
- This technology provides valuable data for precision livestock farming and management.
- The enhanced system addresses key challenges in automated animal counting.
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