Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking
applications
Author
Mihir Durve, Sibilla Orsini, Adriano Tiribocchi, Andrea Montessori, Jean-Michel Tucny, Marco Lauricella, Andrea Camposeo, Dario Pisignano, Sauro Succi
Category
cs.CV
Date Published
2023/01/19
Date Retrieved
2023/01/21
Date Updated
2023/01/21
Description
Tracking droplets in microfluidics is a challenging task. The difficulty
arises in choosing a tool to analyze general microfluidic videos to infer
physical quantities. The state-of-the-art object detector algorithm You Only
Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime
Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet
identification and tracking. The customization includes training YOLO and
DeepSORT networks to identify and track the objects of interest. We trained
several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet
identification and tracking from microfluidic experimental videos. We compare
the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in
terms of training time and time to analyze a given video across various
hardware configurations. Despite the latest YOLOv7 being 10% faster, the
real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU
machine due to additional significant droplet tracking costs arising from the
DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7
networks with DeepSORT in terms of the training time and inference time for a
custom dataset of microfluidic droplets.