Simulation Environment for Neural Network Dataset Generation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10251227" target="_blank" >RIV/61989100:27230/22:10251227 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-98260-7_20" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-98260-7_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-98260-7_20" target="_blank" >10.1007/978-3-030-98260-7_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Simulation Environment for Neural Network Dataset Generation

Popis výsledku v původním jazyce

We present a simulation setup in the robot simulation software CoppeliaSim which is used for a synthetic dataset generation for training the neural network. In the simulator we can generate either color and depth images which can be tuned according to the real cameras mounted to the robot or robotic workplace. Vision sensors capture the simulated scene which contains different environment features, obstacles and objects of interest which can be labeled automatically with another filtering vision sensor. Except static environment which can be imported in case of known setup or generated based on height-field or simple objects. We can simulate randomly or with a specific pose oriented and positioned objects which may appear in the field of view of the robot. As an output the system produce RGB or depth information which is stored as a RGB or a gray-scale image or a combined RGBA image including the RGB data extended by depth data stored in the alpha channel. Second product of the system is a label describing different detectable classes for the neural network. The simulator is able to generate large datasets in a short period of time and produce a highly customized learning base for the neural network.

Název v anglickém jazyce

Simulation Environment for Neural Network Dataset Generation

Popis výsledku anglicky

We present a simulation setup in the robot simulation software CoppeliaSim which is used for a synthetic dataset generation for training the neural network. In the simulator we can generate either color and depth images which can be tuned according to the real cameras mounted to the robot or robotic workplace. Vision sensors capture the simulated scene which contains different environment features, obstacles and objects of interest which can be labeled automatically with another filtering vision sensor. Except static environment which can be imported in case of known setup or generated based on height-field or simple objects. We can simulate randomly or with a specific pose oriented and positioned objects which may appear in the field of view of the robot. As an output the system produce RGB or depth information which is stored as a RGB or a gray-scale image or a combined RGBA image including the RGB data extended by depth data stored in the alpha channel. Second product of the system is a label describing different detectable classes for the neural network. The simulator is able to generate large datasets in a short period of time and produce a highly customized learning base for the neural network.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

<a href="/cs/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: Platforma pro výzkum orientovaný na Průmysl 4.0 a robotiku v ostravské aglomeraci</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 13207

ISBN

978-3-030-98259-1

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

322-332

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Řím

Datum konání akce

13. 10. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000787774900020