# Semi-Automatic Labeling for Deep Learning in Robotics

## TL;DR

This paper introduces ARS, a semi-automatic augmented reality method for rapid, precise dataset labeling in robotics, significantly reducing annotation time and improving detection accuracy for deep learning models.

## Contribution

The paper presents ARS, a novel AR-based semi-automatic labeling technique that drastically reduces annotation time and enhances detection performance in robotics datasets.

## Key findings

- Annotated two large datasets with ARS in less than an hour.
- Achieved a 15% increase in detection precision and recall.
- Enabled training of state-of-the-art detectors with minimal human effort.

## Abstract

In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method which leverages on moving a 2D camera by means of a robot, proving precise camera tracking, and an augmented reality pen to define initial object bounding box, to create large labeled datasets with minimal human intervention. By removing the burden of generating annotated data from humans, we make the Deep Learning technique applied to computer vision, that typically requires very large datasets, truly automated and reliable. With the ARS pipeline, we created effortlessly two novel datasets, one on electromechanical components (industrial scenario) and one on fruits (daily-living scenario), and trained robustly two state-of-the-art object detectors, based on convolutional neural networks, such as YOLO and SSD. With respect to the conventional manual annotation of 1000 frames that takes us slightly more than 10 hours, the proposed approach based on ARS allows annotating 9 sequences of about 35000 frames in less than one hour, with a gain factor of about 450. Moreover, both the precision and recall of object detection is increased by about 15\% with respect to manual labeling. All our software is available as a ROS package in a public repository alongside the novel annotated datasets.