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.