Intravenous (IV) Therapy Infusion Drip Image

We introduce a high-performance computer vision based Intraveneous (IV) infusion speed measurement system as a camera application on an iPhone or Android phone. Our system uses You Only Look Once version 5 (YOLOv5) as it was designed for real-time object detection, making it substantially faster than two-stage algorithms such as R-CNN. In addition, YOLOv5 offers greater precision than its predecessors, making it more competitive with other object detection methods. However, YOLOv5 can be challenging to use on a mobile device for several reasons as it requires substantial computational resources for image processing and prediction generation. Thus, we chose the model optimization approach because it requires the least effort to implement. Because NCNN (Neural Network Computing) is a high-performance neural network inference framework optimized for mobile platforms such as Android and iOS, we converted a YOLOv5 model to an NCNN (Novel Convolutional Neural Network) model. Compared to the previous research, our application showed less variability and higher consistency in the infusion flow rate measurement.