Particle Detection by means of Neural Networks and Synthetic Training Data Refinement in Defocusing Particle Tracking Velocimetry (data)

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)本仓库包含我们提交至2022年《Measurement Science and Technology》期刊主题为“面向流体流动测量的机器学习与数据同化技术”的特刊的论文《散焦粒子跟踪测速技术中基于神经网络与合成训练数据优化的粒子检测方法》（Particle Detection by means of Neural Networks and Synthetic Training Data Refinement in Defocusing Particle Tracking Velocimetry）的补充数据集。该数据集涵盖两类数据：一类是用于训练神经网络以在散焦粒子跟踪测速术（Defocusing Particle Tracking Velocimetry，以下简称DPTV）的记录数据中实现粒子检测的标注图像；另一类是用于训练图像到图像转换网络以生成优化后合成训练数据的未标注粒子图像，相关内容已如论文手稿所述。基于前述数据集训练得到的粒子检测神经网络亦收纳于本仓库中。关于本数据集与训练好的神经网络的使用说明（含示例脚本）可在GitHub仓库https://github.com/MaxDreisbach/DPTV_ML_Particle_detection 中查阅。

搜集汇总

背景与挑战

背景概述

该数据集是2022年Measurement Science and Technology特刊的补充数据，专注于使用神经网络和合成训练数据优化在离焦粒子追踪测速技术中进行粒子检测。它包含用于训练神经网络的标注图像、用于生成合成数据的未标注粒子图像，以及预训练的神经网络模型，总大小为4.2 GB，适用于流体流动测量中的机器学习应用。