labeled Intraveneous (IV) infusion container images

We generated an IV fluid-specific dataset to maximize the accuracy of the measurement. We developed our system as a smartphone application, utilizing the internal camera for the nurses or patients. Thus, users should be able to capture the surface of the fluid in the container by adjusting the smartphone's position or angle to reveal the front view of the container. Thus, we collected the front view of the IV fluid containers when generating the training dataset.We captured the IV fluid container in video mode and extracted an image for every 15 frames. To simulate the actual operation, we changed the capturing angle by moving from the front of the container to the sides, closer, away from the container. We captured the images at a nursing practice lab maintained by the Department of Nursing at Konkuk University Glocal Campus in South Korea. The lab was set up as a mock-up of a general hospital’s infirmary, including the actual IV infusion systems. This process resulted in 2,266 images. We used an iPhone 12 capable of capturing images at FHD (1920*1080) or higher quality. The phone was vertically held when capturing the images. Furthermore, we added 370 IV fluid container images to increase the dataset diversity by searching the web using Google. Thus, there were 2,636 images in the dataset at the end. We used Labelme for image labeling. Labelme is an open-source image annotation tool for object recognition, tracking, and segmentation by enabling the labeling of detailed information about images [16]. We annotated the IV infusion container images as either Vessel or Liquid General as masking coordinates, then stored the outcomes in JSON files, which are input to the computer vision system.为最大化测量精度，我们构建了一款针对静脉输液溶液（IV fluid）的专用数据集。本系统被开发为智能手机应用程序，供护士或患者使用设备内置摄像头完成操作。用户可通过调整智能手机的位置与角度，使容器呈现正面视角，从而拍摄容器内的溶液液面。因此，在构建训练数据集时，我们采集了静脉输液溶液容器的正面视角图像。 我们以视频模式拍摄静脉输液溶液容器，并每15帧提取一张图像。为模拟真实使用场景，我们调整拍摄角度：从容器正前方移至侧面，并调整拍摄距离（拉近、拉远）。本次图像采集工作在韩国建国大学全球校区（Konkuk University Glocal Campus）护理系维护的护理实训实验室中完成。该实验室搭建为综合医院病房的模拟场景，配备了真实的静脉输液系统。该采集流程共获得2266张图像。 我们使用iPhone 12进行拍摄，该设备支持FHD（1920×1080）及更高画质的图像采集。拍摄时，手机保持垂直握持状态。此外，为提升数据集的多样性，我们通过谷歌（Google）网络搜索获取了370张静脉输液溶液容器图像。最终，本数据集总计包含2636张图像。 我们使用Labelme进行图像标注。Labelme是一款开源图像标注工具，支持对图像的详细信息进行标注，可用于目标识别、追踪与分割任务[16]。我们将静脉输液容器图像标注为「容器（Vessel）」或「通用液体（Liquid General）」两类，并以掩膜坐标形式存储标注结果，最终将标注文件保存为JSON格式，作为计算机视觉系统的输入数据。