  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.
