Defect Identification and Measurement using Stereo Vision Camera for In-Line Inspection of Pipeline

Agung Shamsuddin Saragih, Fernaldy Aditya, Waleed Ahmed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In its operation, pipelines encounter a variety of damages, from improper application and unfavorable environmental conditions, which causes defects like metal loss, corrosion, cracks, among others. Along with the growing use of mobile robotic systems for pipelines inspection, we proposed a stereo camera-based monitoring system that can scan, detect, locate, and measure internal defects, particularly on cracks and leakage. To achieve autonomy, the system utilizes a stereo camera to extract 3D information, while a deep learning algorithm, namely Convolutional Neural Network (CNN), is used to identify the defect classes. The result demonstrates the generation of 3D point clouds, classification, and defect quantification. This paper aims to cover the device specification, control solution, system performance, as well as current drawbacks and enhancement approaches.

Original languageEnglish
Title of host publication2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665418010
DOIs
Publication statusPublished - 18 Mar 2022
Event2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 - Dubai, United Arab Emirates
Duration: 21 Feb 202224 Feb 2022

Publication series

Name2022 Advances in Science and Engineering Technology International Conferences, ASET 2022

Conference

Conference2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
Country/TerritoryUnited Arab Emirates
CityDubai
Period21/02/2224/02/22

Keywords

  • computer vision
  • defects
  • inline inspection
  • measurement
  • pipeline
  • stereo camera

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