基于改进YOLOv8n的高精度输液监测方法

High-precision infusion monitoring method based on improved YOLOv8n

针对视觉传感器输液监测方式精度不足与传感器不便安装的问题,提出一种基于改进YOLOv8n网络的高精度输液监测方法。在原有网络基础上,使用PuzzleMix数据增强,提高网络泛化能力并避免关键特征被裁剪;引入层次筛选特征金字塔结构,降低参数量的同时增强对液滴特征的表达能力;添加混合局部通道注意力机制,加强液滴特征提取;提出一种Inner-PIoU损失函数,通过辅助回归框使回归效果更优,提升了精度。同时,提出一种使用检测框几何参数比值的方法精确测定输液速度与输液余量。实验结果表明,改进网络与YOLOv8n相比,mAP@0.5:0.95提升了2.674%,且模型大小仅3.87M。在多种复杂输液环境中,所提方法能够实现高精度监测输液速度与输液余量。

A high-precision infusion monitoring method based on improved YOLOv8n network was proposed to address the difficulties of insufficient accuracy and inconvenient installation of visual sensor infusion monitoring methods.Based on the original network,the PuzzleMix data augmentation method was used to improve its generalization ability and to avoid cutting key features.The high-level screening-feature fusion pyramid networks structure was introduced to reduce the number of parameters and enhance the expression ability of droplet features.The Mixed local channel attention was included to enhance droplet feature extraction.The Inner-PIoU was proposed to improve the loss function,which utilized auxiliary regression anchor box to improve regression performance and accuracy.Meanwhile,a method used the ratio of geometric parameters of the detection box was proposed for accurately measuring infusion speed and the remainder.The experimental results show that compared with YOLOv8n,the mAP@0.5:0.95 is increased by 2.674%,and the model size is only 3.87 M.In various complex infusion environments,the proposed method can achieve accurate monitoring of infusion speed and the remainder.