Instance Segmentation for Droplet Through Sessile Drop Technique

This paper describes a dataset of droplet images captured using the sessile drop technique, intended for applications in wettability analysis, surface characterization, and machine learning model training. The dataset comprises both original and synthetically augmented images to enhance its diversity and robustness for training machine learning models. The original, non-augmented portion of the dataset consists of 420 images of sessile droplets. To increase the dataset size and variability, an augmentation process was applied, generating 1008 additional images. This augmentation employed adjustments to brightness (±18%) and exposure (±10%), simulating variations in lighting conditions during image acquisition. The combined dataset, totaling 1428 images, offers a valuable resource for developing and evaluating automated droplet analysis methods, particularly those based on deep learning. This abstract provides an overview of the dataset's composition and augmentation strategy, highlighting its potential contribution to the field of surface science and image analysis.