Making Object Detection Available to Everyone—A Hardware Prototype for Semi-automatic Synthetic Data Generation

Besginow, Andreas; Büttner, Sebastian; Röcker, Carsten

doi:10.1007/978-3-030-50344-4_14

Cited by 1 publication

(1 citation statement)

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“…Besginow et al [9] propose an alternative approach that generates labeled data for a single object. They use a hardware setup to capture an object from multiple angles.…”

Section: Related Workmentioning

confidence: 99%

Automatic Generation of Machine Learning Synthetic Data Using ROS

Hart

Goodman

O'Shea

2021

Lecture Notes in Computer Science

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Data labeling is a time intensive process. As such, many data scientists use various tools to aid in the data generation and labeling process. While these tools help automate labeling, many still require user interaction throughout the process. Additionally, most target only a few network frameworks. Any researchers exploring multiple frameworks must find additional tools or write conversion scripts. This paper presents an automated tool for generating synthetic data in arbitrary network formats. It uses Robot Operating System (ROS) and Gazebo, which are common tools in the robotics community. Through ROS paradigms, it allows extensive user customization of the simulation environment and data generation process. Additionally, a plugin-like framework allows the development of arbitrary data format writers without the need to change the main body of code. Using this tool, the authors were able to generate an arbitrarily large image dataset for three unique training formats using approximately 15 minutes of user setup time and a variable amount of hands-off run time, depending on the dataset size. The source code for this data generation tool is

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“…Besginow et al [9] propose an alternative approach that generates labeled data for a single object. They use a hardware setup to capture an object from multiple angles.…”

Section: Related Workmentioning

confidence: 99%