Grasping Point Detection Using Monocular Camera Image Processing and Knowledge of Center of Gravity

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F22%3A39919571" target="_blank" >RIV/00216275:25530/22:39919571 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-09076-9_48" target="_blank" >http://dx.doi.org/10.1007/978-3-031-09076-9_48</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-09076-9_48" target="_blank" >10.1007/978-3-031-09076-9_48</a>

Result language
angličtina
Original language name
Grasping Point Detection Using Monocular Camera Image Processing and Knowledge of Center of Gravity
Original language description
The ability to grasp objects is one of the basic functions of modern industrial robots. In this article, the focus is placed on a system for processing the image provided by a robot visual perception system leading to the detection of objects grasping points. The proposed processing system is based on a multi-step method using convolutional neural networks (CNN). The first step is to use the first CNN to transform the input image into a schematic image with labeled objects centers of gravity, which then serves as a supporting input to the second CNN. In this second CNN, original input and supporting input images are used to obtain a schematic image containing the grasping points of the objects. This solution is further compared with a network providing grasping points directly from the input image. As a result, the proposed method provided a 0.7% improvement in the average intersection over union for all of the models.
Czech name
—
Czech description
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Project
<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Artificial Intelligence Trends in Systems : proceedings of 11th Computer science on-line conference 2022, Vol. 2
ISBN
978-3-031-09075-2
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
11
Pages from-to
531-541
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
ONLINE
Event date
Apr 26, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000893642100048

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