Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry (data)

TechnicalRemarks: This repository contains the supplementary data to our contribution "Particle Detection by means of Neural Networks and Synthetic Training Data Refinement in Defocusing Particle Tracking Velocimetry" to the 2022 Measurement Science and Technology special issue on the topic “Machine Learning and Data Assimilation techniques for fluid flow measurements”. This data includes annotated images used for the training of neural networks for particle detection on DPTV recordings as well as unannotated particle images used for training of the image-to-image translation networks for the generation of refined synthetic training data, as presented in the manuscript. The neural networks for particle detection trained on the aforementioned data are contained in this repository as well.

An explanation on the use of this data and the trained neural networks, containing an example script can be found on GitHub (https://github.com/MaxDreisbach/DPTV_ML_Particle_detection)

Cite this as

Dreisbach, Maximilian, Leister, Robin, Probst, Matthias, Friederich, Pascal, Stroh, Alexander, Kriegseis, Jochen (2023). Dataset: Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry (data). https://doi.org/10.35097/1333

DOI retrieved: 2023

Additional Info

Field Value
Imported on August 4, 2023
Last update August 4, 2023
License CC BY 4.0 Attribution
Source https://doi.org/10.35097/1333
Author Dreisbach, Maximilian
More Authors
Leister, Robin
Probst, Matthias
Friederich, Pascal
Stroh, Alexander
Kriegseis, Jochen
Source Creation 2023
Publishers
Karlsruhe Institute of Technology
Production Year 2022
Publication Year 2023
Subject Areas
Name: Engineering