Identification of Objects in Oilfield Infrastructure using Engineering Diagram and Machine Learning Methods

Muhammad Hami Asma i.Bin Ismail

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

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.

Язык оригиналаанглийский
Название основной публикацииISCI 2021 - 2021 IEEE Symposium on Computers and Informatics
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы19-24
Число страниц6
ISBN (электронное издание)978-1-6654-0276-7
DOI
СостояниеОпубликовано - 2021
Событие2021 IEEE Symposium on Computers and Informatics, ISCI 2021 - Kuala Lumpur, Малайзия
Продолжительность: 16 окт 2021 → …

Серия публикаций

НазваниеISCI 2021 - 2021 IEEE Symposium on Computers and Informatics

Конференция

Конференция2021 IEEE Symposium on Computers and Informatics, ISCI 2021
СтранаМалайзия
ГородKuala Lumpur
Период16.10.2021 → …

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