基于改进YOLO v8s的母猪分娩结束识别方法OA

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.