Object detection for robotic grasping using a cascade of convolutional networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F23%3A39920966" target="_blank" >RIV/00216275:25530/23:39920966 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/10217360" target="_blank" >https://ieeexplore.ieee.org/document/10217360</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PC58330.2023.10217360" target="_blank" >10.1109/PC58330.2023.10217360</a>

Result language

angličtina

Original language name

Object detection for robotic grasping using a cascade of convolutional networks

Original language description

Robot guidance in industry is a significant issue that needs to be dealt with in modern manufacturing facilities. One of the common tasks in this area is the pick and place problem. For proper implementation of an automatic pick and place application using a robotic arm for object grasping, it is necessary to detect the accurate pose of the objects of interest. In this contribution, a novel engineering approach to object positioning, based on image processing is proposed. In this approach, the operation is composed of a cascade of convolutional neural networks. This cascade consists of 2 different types of networks. The first one is the object detection network called YOLOv5. It is used to process the raw image data from the scene to provide precise localization and determine the position of the objects of interest. After that, crops of the detected objects are created and processed by the second neural network, namely EfficientNet. This classification network is used to determine the rotation angle of the detected objects. The proposed approach provides a precision rate of 0.997 and a recall rate of 0.999 for locating and determining the correct position. For angle classification, EfficientNet provides an accuracy of 0.951. All tests are performed on the testing set of the legitimate positioning problem.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20205 - Automation and control systems

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Article name in the collection

Process control 23 : proceedings of the 2023 24th international conference on process control (PC)

ISBN

979-8-3503-4762-3

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

198-202

Publisher name

IEEE (Institute of Electrical and Electronics Engineers)

Place of publication

New York

Event location

Štrbské Pleso

Event date

Jun 6, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001058530100034