一种用于多层楼房养殖场猪只自动轻量计数算法研究

doi:10.16174/j.issn.1673-4645.2026.04.003

Abstract

Abstract: To address prominent issues such as the low efficiency of manual counting operations, large counting deviations, and the susceptibility to stress responses in pig herds, this paper proposed a lightweight pig detection and counting model, PS-YOLO (Pig shipment YOLO). This model was based on the YOLOv26n architecture, incorporating the lightweight EGC-Net structure (ECA-GhostConv) into the backbone network, and adopting a linear warm-up training strategy, conducting sensitivity analysis on the warm-up epochs parameters. Experimental results showed that when the warm-up epochs of PS-YOLO were set to 135, the model exhibited optimal performance, with mAP50 and mAP50-95 reaching 0.989 and 0.749, respectively. Compared to the baseline YOLOv26n, PS-YOLO reduced parameter count, computation, and model size by 13.0%, 13.8% and 11.1%, respectively, effectively lowering model complexity and deployment costs while maintaining high detection accuracy. Compared with mainstream lightweight models such as YOLOv5n, YOLOv8n and YOLOv11n, PS-YOLO demonstrates superior lightweight and real-time detection performance in scenarios with complex occlusion and dense transportation, providing a low-cost, high-efficiency technical solution for the intelligent management of pig transfers in multi-story pig farms.

Key words: pig, multi-layer breeding, automatic counting, PS-YOLO, lightweight, linear warm-up

CLC Number:

S828

YUAN Peiyin, YANG Lei, YANG Peixin, PANG Wulin, ZHONG Ruqing, LIU Xiangdong. Research on an automatic lightweight counting algorithm for pigs in multi-story farm buildings[J].China Swine Industry, 2026, 21(4): 87-101.

References

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Research on an automatic lightweight counting algorithm for pigs in multi-story farm buildings

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