Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods. / Ismail, Muhammad Hami Asma i.Bin.
ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics. Institute of Electrical and Electronics Engineers Inc., 2021. стр. 19-24 (ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods
AU - Ismail, Muhammad Hami Asma i.Bin
N1 - Publisher Copyright: © 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper describes the effort of implementation of object detection architectures into data mining for physical asset management purpose. Data mining in asset management often relates to the activity of recovering information from engineering diagrams in PDF format such as Piping and Instrumentation Diagram (PID). The existing study around the world revolves around the basic component detections without the aim to produce a practical methodology for usage in industry. This study started with how the final output should be according to normal industry practice and standards such as ISO14224. It is hypothesized that a good pre-trained model for infrastructure detection on PID can be developed to suit industrial needs. Three different deep learning architectures were used in the study are Faster R-CNN, YOLOv3 and Yolov5. YOLOv3 and Faster R-CNN provides much consistent training results compared to Yolov5, hence they are better suited for further development.
AB - This paper describes the effort of implementation of object detection architectures into data mining for physical asset management purpose. Data mining in asset management often relates to the activity of recovering information from engineering diagrams in PDF format such as Piping and Instrumentation Diagram (PID). The existing study around the world revolves around the basic component detections without the aim to produce a practical methodology for usage in industry. This study started with how the final output should be according to normal industry practice and standards such as ISO14224. It is hypothesized that a good pre-trained model for infrastructure detection on PID can be developed to suit industrial needs. Three different deep learning architectures were used in the study are Faster R-CNN, YOLOv3 and Yolov5. YOLOv3 and Faster R-CNN provides much consistent training results compared to Yolov5, hence they are better suited for further development.
KW - Asset Management
KW - CMMS
KW - Data Mining
KW - Engineering Drawing
KW - ISO14224
UR - http://www.scopus.com/inward/record.url?scp=85123843857&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/d276a1f2-54f4-373a-a0ef-9d446ae21071/
U2 - 10.1109/ISCI51925.2021.9633745
DO - 10.1109/ISCI51925.2021.9633745
M3 - Conference contribution
AN - SCOPUS:85123843857
SN - 9781665402767
T3 - ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics
SP - 19
EP - 24
BT - ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Symposium on Computers and Informatics, ISCI 2021
Y2 - 16 October 2021
ER -
ID: 35609258