BYG-drop: a tool for enhanced droplet detection in liquid–liquid systems through machine learning and synthetic imaging

Abstract

A new image processing machine learning algorithm for droplet detection in liquid–liquid systems is here introduced. The method combines three key numerical tools—YOLOv5 for object detection, Blender for synthetic image generation, and CycleGAN for image texturing—and was named “BYG-Drop for Blender-YOLO-CycleGAn” droplet detection. BYG-Drop outperforms traditional image processing techniques in both accuracy and number of droplets detected in digital test cases. When applied to experimental images, it remains consistent with established techniques such as laser diffraction while outperforming other image processing techniques in droplet detection accuracy. The use of synthetic images for training also provides advantages such as training on a large labeled dataset, which prevents false detections. CycleGAN’s texturing also allows quick adaptation to different fluid systems, increasing the versatility of image processing in drop size distribution measurement. Finally, the processing time per image is significantly faster with this approach.