Details
Original language | English |
---|---|
Title of host publication | 2024 IEEE 17th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET) |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 534-539 |
Number of pages | 6 |
ISBN (electronic) | 9798331520564 |
ISBN (print) | 979-8-3315-2057-1 |
Publication status | Published - 2024 |
Event | 17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024 - Lviv, Ukraine Duration: 8 Oct 2024 → 12 Oct 2024 |
Abstract
In this work, the previously proposed structural scheme of a virtual assistant is intended for training biomedical engineers and other specialists in working with specialized high-tech equipment, using a cryolaboratory as an example. The necessity of incorporating a specialized detection stage, which should consider the specific visual characteristics of the particular laboratory, has been demonstrated. Three models were trained on the R-CNN architecture (based on the Detectron2 framework) to create a virtual guide for a 3D cryolaboratory. The metrics of the obtained models were analyzed, and their suitability for specific tasks was evaluated. The obtained results indicate sufficient detection performance of the cryo-laboratory components, allowing the use of the developed models to create a specialized virtual assistant.
Keywords
- biomedical engineering, cryo laborato ry, deep learning, Detectron2, education, health care, object detection
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Electrical and Electronic Engineering
- Mathematics(all)
- Computational Mathematics
- Physics and Astronomy(all)
- Instrumentation
Cite this
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2024 IEEE 17th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). Institute of Electrical and Electronics Engineers Inc., 2024. p. 534-539.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Object Detection for Virtual Assistant in Cryolaboratory Based on Detectron2 Framework
AU - Lytvyn, Anastasiia
AU - Posokhova, Kateryna
AU - Tymkovych, Maksym
AU - Avrunin, Oleg
AU - Hubenia, Oleksandra
AU - Glasmacher, Birgit
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In this work, the previously proposed structural scheme of a virtual assistant is intended for training biomedical engineers and other specialists in working with specialized high-tech equipment, using a cryolaboratory as an example. The necessity of incorporating a specialized detection stage, which should consider the specific visual characteristics of the particular laboratory, has been demonstrated. Three models were trained on the R-CNN architecture (based on the Detectron2 framework) to create a virtual guide for a 3D cryolaboratory. The metrics of the obtained models were analyzed, and their suitability for specific tasks was evaluated. The obtained results indicate sufficient detection performance of the cryo-laboratory components, allowing the use of the developed models to create a specialized virtual assistant.
AB - In this work, the previously proposed structural scheme of a virtual assistant is intended for training biomedical engineers and other specialists in working with specialized high-tech equipment, using a cryolaboratory as an example. The necessity of incorporating a specialized detection stage, which should consider the specific visual characteristics of the particular laboratory, has been demonstrated. Three models were trained on the R-CNN architecture (based on the Detectron2 framework) to create a virtual guide for a 3D cryolaboratory. The metrics of the obtained models were analyzed, and their suitability for specific tasks was evaluated. The obtained results indicate sufficient detection performance of the cryo-laboratory components, allowing the use of the developed models to create a specialized virtual assistant.
KW - biomedical engineering
KW - cryo laborato ry
KW - deep learning
KW - Detectron2
KW - education
KW - health care
KW - object detection
UR - http://www.scopus.com/inward/record.url?scp=85212405748&partnerID=8YFLogxK
U2 - 10.1109/TCSET64720.2024.10755685
DO - 10.1109/TCSET64720.2024.10755685
M3 - Conference contribution
AN - SCOPUS:85212405748
SN - 979-8-3315-2057-1
SP - 534
EP - 539
BT - 2024 IEEE 17th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024
Y2 - 8 October 2024 through 12 October 2024
ER -