Application of deep learning and inline holography to estimate the droplet size distribution

Abstract

We examine five machine learning-based architectures to estimate the droplet size distributions obtained using digital inline holography. The architectures, namely, U-Net, R2 U-Net, Attention U-Net, V-Net, and Residual U-Net, are trained using synthetic holographic images. Our assessment focuses on evaluating the training, validation, and prediction performance of these architectures. Our results indicate that U-Net and R2 U-Net are the most proficient architectures, consistently demonstrating superior performance trends and achieving the highest Intersection Over Union (IOU) scores among the five architectures studied. We employ additional training using experimental holographic images for the two top-performing architectures to validate their efficacy further. Subsequently, they are employed to segment an experimental dataset illustrating the bag breakup phenomenon, facilitating the extraction of size distribution. The extracted size distribution from U-Net and R2 U-Net segmentation is then compared with the analytical model proposed by Jackiw and Ashgriz (2022) by employing the gamma and log-normal distributions. Our findings indicate that the gamma distribution provides a more accurate prediction of the multi-modal size distribution than the log-normal distribution owing to its long exponential tail. The present study offers valuable insights into the effectiveness of machine learning architectures in estimating particle/droplet sizes, highlighting their practical application in real-world experimental scenarios.