农业机械学报2026，Vol.57Issue(15)：46-55,10.DOI:10.6041/j.issn.1000-1298.2026.15.004
基于改进YOLO v8s的母猪分娩结束识别方法
Method for Identifying End of Sow Farrowing Based on Improved YOLO v8s Model
摘要
Abstract
In automatic monitoring of sow farrowing,the end of parturition is usually determined indirectly by identifying the birth of the last piglet.However,this approach suffers from poor real-time performance,low detection accuracy,and strong susceptibility to occlusion,making it unsuitable for production needs.To address these issues,a farrowing-end detection method that directly identified the placenta was proposed based on an improved YOLO v8s model.By incorporating the Focus module,SE attention mechanism,C2f-SCConv module,and BiFPN-P2 structure,the model was optimized in feature representation,multi-scale information fusion,and lightweight design.Ablation experiments showed that the SE attention mechanism and C2f-SCConv module significantly improved the detection accuracy of small-scale and low-contrast targets,while the BiFPN-P2 structure effectively reduced the number of parameters and computational complexity without sacrificing precision.The improved YOLO v8s model achieved 98.1%precision,94.9%recall,and 98.8%mAP,with only 7.33×106 parameters and an inference speed of 61.71 f/s.Compared with mainstream models such as NanoDet,RT-DETR,Faster R-CNN,YOLO v5,and the original YOLO v8s,the proposed method achieved the best detection accuracy while maintaining excellent real-time performance.Furthermore,a temporal judgment mechanism based on video frame intervals and frame-level counters was developed to accurately recognize the end of farrowing in real farrowing videos.Under a 25 f interval,the average time error was 5.98 s,which was further reduced to 1.84 s at a 5 f interval,significantly improving both timeliness and accuracy.The proposed method overcame the limitations of piglet-based indirect inference and extended from image-level detection to video-level temporal event recognition.关键词
母猪分娩/胎衣检测/YOLO v8s/目标检测/视频级识别/智能养殖Key words
sow farrowing/placenta detection/YOLO v8s/object detection/video-level recognition/intelligent farming分类
农业科技引用本文复制引用
祝志慧,韩雨彤,侯文烁,黎煊,徐学文,徐迪红..基于改进YOLO v8s的母猪分娩结束识别方法[J].农业机械学报,2026,57(15):46-55,10.基金项目
湖北省支持种业高质量发展资金项目(HBZY2023B006-03) （HBZY2023B006-03）
