Lee Xian Yeow, Saha Sourabh K, Sarkar Soumik, Giera Brian
Department of Mechanical Engineering, Iowa State University, United States.
G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, United States.
Data Brief. 2020 Aug 3;32:106119. doi: 10.1016/j.dib.2020.106119. eCollection 2020 Oct.
This document describes the collection and organization of a dataset that consists of raw videos and extracted sub-images from video frames of a promising additive manufacturing technique called Two-Photon Lithography (TPL). Four unprocessed videos were collected, with each video capturing the printing process of different combinations of 3D parts on different photoresists at varying light dosages. These videos were further trimmed to obtain short clips that are organized by experimental parameters. Additionally, this dataset also contains a python script to reproduce an organized directory of cropped video frames extracted from the trimmed videos. These cropped frames focus on a region of interest around the parts being printed. We envision that the raw videos and cropped frames provided in this dataset will be used to train various computer vision and machine learning algorithms for applications such as object segmentation and localization applications. The cropped video frames were manually labelled by an expert to determine the quality of the printed part and for printing parameter optimization as presented in "Automated Detection of Part Quality during Two-Photon Lithography via Deep Learning" [1].
本文档描述了一个数据集的收集和整理，该数据集由原始视频以及从一种名为双光子光刻（TPL）的有前景的增材制造技术的视频帧中提取的子图像组成。收集了四个未处理的视频，每个视频在不同光剂量下捕捉了不同3D部件在不同光刻胶上的打印过程。这些视频进一步被剪辑成按实验参数组织的短片。此外，该数据集还包含一个Python脚本，用于重现从剪辑后的视频中提取的裁剪视频帧的有组织目录。这些裁剪后的帧聚焦于正在打印部件周围的感兴趣区域。我们设想，此数据集中提供的原始视频和裁剪后的帧将用于训练各种计算机视觉和机器学习算法，以用于诸如目标分割和定位应用等。裁剪后的视频帧由一位专家手动标注，以确定打印部件的质量，并如《通过深度学习在双光子光刻过程中自动检测部件质量》[1]中所述用于打印参数优化。
