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Image pre-processing and segmentation for real-time subsea corrosion inspection.

Pirie, Craig; Moreno-Garcia, Carlos Francisco

Authors

Craig Pirie



Contributors

Lazaros Iliadis
Editor

John Macintyre
Editor

Chrisina Jayne
Editor

Elias Pimenidis
Editor

Abstract

Inspection engineering is a highly important field in the Oil & Gas sector for analysing the health of offshore assets. Corrosion, a naturally occurring phenomenon, arises as a result of a chemical reaction between a metal and its environment, causing it to degrade over time. Costing the global economy an estimated US $2.5 Trillion per annum, the destructive nature of corrosion is evident. Following the downturn endured by the industry in recent times, the need to combat corrosion is escalated, as companies look to cut costs by increasing efficiency of operations without compromising critical processes. This paper presents a step towards automating solutions for real-time inspection using state-of-the-art computer vision and deep learning techniques. Experiments concluded that there is potential for the application of computer vision in the inspection domain. In particular, Mask R-CNN applied on the original images (i.e. without any form of pre-processing) was found to be most viable solution, with the results showing a mAP of 77.1%.

Citation

PIRIE, C. and MORENO-GARCIA, C.F. 2021. Image pre-processing and segmentation for real-time subsea corrosion inspection. In Iliadis, L., Macintyre, J., Jayne, C. and Pimenidis, E. (eds.). Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021), 25-27 June 2021, Halkidiki, Greece. Proceedings of the International Neural Networks Society (INNS), 3. Cham: Springer [online], pages 220-231. Available from: https://doi.org/10.1007/978-3-030-80568-5_19

Conference Name 22nd Enginering applications of neural networks conference (EANN2021)
Conference Location Halkidiki, Greece
Start Date Jun 25, 2021
End Date Jun 27, 2021
Acceptance Date Apr 7, 2021
Online Publication Date Jul 1, 2021
Publication Date Dec 31, 2021
Deposit Date Jun 25, 2021
Publicly Available Date Jul 2, 2022
Publisher Springer
Pages 220-231
Series Title Proceedings of the International Neural Networks Society (INNS)
Series Number 3
Series ISSN 2661-8141
Book Title Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021)
ISBN 9783030805678
DOI https://doi.org/10.1007/978-3-030-80568-5_19
Keywords Corrosion; Inspection; Subsea; Segmentation; Real-time recognition
Public URL https://rgu-repository.worktribe.com/output/1369876