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Image Pre-processing and Segmentation for Real-Time Subsea Corrosion Inspection

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Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN 2021)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 3))

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%.

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Notes

  1. 1.

    https://oilandgasuk.co.uk/oil-gas-uk-figures-show-impact-of-oil-price-downturn-on-jobs/.

  2. 2.

    The dataset can be downloaded from https://drive.google.com/drive/folders/1dbOVdg5x75brUAwuI2X6voIMEwJzfiYX?usp=sharing.

  3. 3.

    Experiments were run on a MacBook Pro with a 2.3 GHz Dual-Core Intel Core i5 processor, 8 GB 2133 MHz LPDDR3 memory and an Intel Iris Plus Graphics 640 1536 MB graphics card.

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Authors and Affiliations

  1. Robert Gordon University, Garthdee Road, Aberdeen, AB10 7GJ, UK

    Craig Pirie & Carlos Francisco Moreno-Garcia

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  1. Craig Pirie
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  2. Carlos Francisco Moreno-Garcia
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Correspondence to Carlos Francisco Moreno-Garcia .

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Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Pirie, C., Moreno-Garcia, C.F. (2021). Image Pre-processing and Segmentation for Real-Time Subsea Corrosion Inspection. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_19

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