# DeepScanner: a Robotic System for Automated 2D Object Dataset Collection with Annotations

arXiv: https://arxiv.org/abs/2108.02555 · HTML: https://mlsanity.com/paper/2108.02555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences](https://mlsanity.com/paper/2102.10820/) (2021)
- [Semi-Automatic Labeling for Deep Learning in Robotics](https://mlsanity.com/paper/1908.01862/) (2019)

## Abstract (arXiv)

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.