Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DropTrack -- automatic droplet tracking using deep learning for
  microfluidic applications   Deep neural networks are rapidly emerging as data analysis tools, often
outperforming the conventional techniques used in complex microfluidic systems.
One fundamental analysis frequently desired in microfluidic experiments is
counting and tracking the droplets. Specifically, droplet tracking in dense
emulsions is challenging as droplets move in tightly packed configurations.
Sometimes the individual droplets in these dense clusters are hard to resolve,
even for a human observer. Here, two deep learning-based cutting-edge
algorithms for object detection (YOLO) and object tracking (DeepSORT) are
combined into a single image analysis tool, DropTrack, to track droplets in
microfluidic experiments. DropTrack analyzes input videos, extracts droplets'
trajectories, and infers other observables of interest, such as droplet
numbers. Training an object detector network for droplet recognition with
manually annotated images is a labor-intensive task and a persistent
bottleneck. This work partly resolves this problem by training object detector
networks (YOLOv5) with hybrid datasets containing real and synthetic images. We
present an analysis of a double emulsion experiment as a case study to measure
DropTrack's performance. For our test case, the YOLO networks trained with 60%
synthetic images show similar performance in droplet counting as with the one
trained using 100% real images, meanwhile saving the image annotation work by
60%. DropTrack's performance is measured in terms of mean average precision
(mAP), mean square error in counting the droplets, and inference speed. The
fastest configuration of DropTrack runs inference at about 30 frames per
second, well within the standards for real-time image analysis.

37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess