Droplet Tracking

Droplet tracking involves automatically identifying and following individual droplets within fluids, a crucial task in various scientific and engineering fields. Current research heavily utilizes deep learning, particularly object detection models like YOLO (in versions 5 and 7) coupled with tracking algorithms such as DeepSORT, to analyze high-speed video data from experiments ranging from microfluidics to complex systems like walking droplets. These methods are being improved through techniques like synthetic data generation to address data scarcity and enhance model performance, ultimately enabling faster, more accurate analysis of droplet behavior. The resulting advancements are impacting diverse applications, including precision agriculture and the study of complex fluid dynamics.