In the proposed study, we describe the possibility of automated dataset collection using an articulated robot. The proposed technology reduces the number of pixel errors on a polygonal dataset and the time spent on manual labeling of 2D objects. The paper describes a novel automatic dataset collection and annotation system, and compares the results of automated and manual dataset labeling. Our approach increases the speed of data labeling 240-fold, and improves the accuracy compared to manual labeling 13-fold. We also present a comparison of metrics for training a neural network on a manually annotated and an automatically collected dataset.
@article{arxiv.2108.02555,
title = {DeepScanner: a Robotic System for Automated 2D Object Dataset Collection with Annotations},
author = {Valery Ilin and Ivan Kalinov and Pavel Karpyshev and Dzmitry Tsetserukou},
journal= {arXiv preprint arXiv:2108.02555},
year = {2021}
}
Comments
Accepted to 26th International Conference on Emerging Technologies and Factory Automation (ETFA) 2021, IEEE copyright, 8 pages, 10 figures