Standard

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. p. 19-24 (ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Ismail, MHAIB 2021, Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods. in ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics. ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics, Institute of Electrical and Electronics Engineers Inc., pp. 19-24, 2021 IEEE Symposium on Computers and Informatics, ISCI 2021, Kuala Lumpur, Malaysia, 16.10.2021. https://doi.org/10.1109/ISCI51925.2021.9633745

APA

Ismail, M. H. A. I. B. (2021). Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods. In ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics (pp. 19-24). (ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCI51925.2021.9633745

Vancouver

Ismail MHAIB. Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods. In ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics. Institute of Electrical and Electronics Engineers Inc. 2021. p. 19-24. (ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics). doi: 10.1109/ISCI51925.2021.9633745

Author

Ismail, Muhammad Hami Asma i.Bin. / Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods. ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 19-24 (ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics).

BibTeX

@inproceedings{8676e4bab6d143aea5d85737f7175d7d,
title = "Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods",
abstract = "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.",
keywords = "Asset Management, CMMS, Data Mining, Engineering Drawing, ISO14224",
author = "Ismail, {Muhammad Hami Asma i.Bin}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Symposium on Computers and Informatics, ISCI 2021 ; Conference date: 16-10-2021",
year = "2021",
doi = "10.1109/ISCI51925.2021.9633745",
language = "English",
isbn = "9781665402767",
series = "ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "19--24",
booktitle = "ISCI 2021 - 2021 IEEE Symposium on Computers and Informatics",
address = "United States",

}

RIS

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