High Precision Intravenous Drip Infusion Monitoring Method Based on Improved YOLOv8n

Visual sensor-based infusion monitoring methods have the advantage of being easily integrated into existing medical facilities, but suffer from the problem that monitoring accuracy decreases when the distance between dropper and camera changes. Therefore, this paper proposed a deep learning-based infusion monitoring method that uses improved YOLOv8n network to detect the dropper, droplet, and liquid level, obtains the relative droplet area and relative liquid level height from the ratio of geometric parameters of the detection anchor box, and calculates the infusion rate by the periodic change of the relative droplet area. The experimental results show that the mAP@0.5:0.95 of the improved network reached 91.739%, using relative droplet area to calculate the infusion rate is more accurate than previous method using droplet area, and the error did not exceed 1 drop per minute when the infusion rate is less than 120 drops per minute. It can also accurately measure the infusion remainder in the dropper when the infusion is about to end, and remind the medical staff to change the bottle or remove the needle at the right time.