"Image Dataset for Intravenous (IV) Drop Detection with Dual Annotations (Bounding Box and Heatmap)"

"This dataset contains 3,458 high-resolution, annotated images designed to facilitate research and development in automated, computer vision-based monitoring of intravenous (IV) therapy. It was created to address the lack of diverse, publicly available datasets for the specific task of IV fluid drop detection. The images were captured under a wide range of realistic conditions, including varied ambient and artificial lighting, diverse clinical and non-clinical backgrounds, and multiple fluid types (e.g., clear saline, blood, and other colored solutions). The dataset also includes a significant number of negative samples (frames without drops) to enable the training of robust models that can avoid false positives from common artifacts like air bubbles or light reflections.The primary contribution of this dataset is its dual-annotation format, making it uniquely suitable for training and directly comparing two distinct detection paradigms:Bounding Box Labels: A YOLO-format .txt file is provided for each image, containing standardized bounding box coordinates for traditional object detection models.Heatmap Targets: A corresponding 26x26 NumPy (.npy) array is provided for each image, representing a ground-truth heatmap with a 2D Gaussian peak at the drop's center. This is ideal for training point-event or regression-based detection models.This dual format enables researchers to benchmark different algorithmic approaches on an identical, challenging, and diverse set of images. The data was used for the comparative study presented in the paper \"A Dual-Module Computer Vision Approach for Real-Time IV Drip Rate and Fluid Level Monitoring.\""