Fully convolutional networks-based particle distribution analysis at multiphase interfaces

Abstract

The intricate interplay between droplet dynamics and particle characteristics has led to the extensive use of particle-laden droplets in diverse applications, spanning industrial engineering and scientific research. Accurate particle distribution analysis at multiphase interfaces is crucial for understanding the complex behaviors of these soft matters, for example, liquid marbles (LMs). However, traditional image analysis methods for particle-laden interfaces often rely on manual processing, which is both time-consuming and susceptible to human error. In this study, we present a pixel-level image recognition technique based on fully convolutional networks (FCNs) to automatically analyze the interfacial particle distribution of LMs exhibiting clear core-shell structures. The FCNs-based method enables efficient and accurate segmentation of target particles on sessile LMs, utilizing experimental data obtained from an in-situ optical imaging device. This approach significantly expedites the analysis of particle distribution, including proportion, concentration, and cluster centers. We assess the performance of the proposed method using image data of monolayer LMs encapsulated by dispersed polystyrene microspheres, demonstrating its superiority over traditional techniques in terms of accuracy and generalization. This investigation aims to offer novel insights into determining pertinent particulate characteristics within similar solid-liquid hybrid microsystems.