Skip to main content
Springer Nature Link
Log in

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

  • Conference paper
  • First Online:
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))

  • 1082 Accesses

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%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

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.

References

  1. Yang, Y., Khan, F., Thodi, P., Abbassi, R.: 2021 Corrosion induced failure analysis of subsea pipelines (2021)

    Google Scholar 

  2. Anantharaman, R., Velazquez, M., Lee, Y.: Utilizing mask R-CNN for detection and segmentation of oral diseases. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2018, pp. 2197–2204 (2018). https://doi.org/10.1109/BIBM.2018.8621112

  3. Wu, F., Jin, G., Gao, M., He, Z., Yang, Y.: Helmet detection based on improved YOLO V3 deep model. In: 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), Banff, AB, Canada, 2019, pp. 363–368 (2019). https://doi.org/10.1109/ICNSC.2019.8743246

  4. Badue, C., et al.: A 2021 Self-driving cars: a survey (2021)

    Google Scholar 

  5. Szymak, P., Piskur, P., Naus, K.: The effectiveness of using a pretrained deep learning neural networks for object classification in underwater video. Remote Sens. 12(18), 1–19 (2020)

    Article  Google Scholar 

  6. Arnold-Bos, A., Malkasse, J.-P., Kervern, G.: A preprocessing framework for automatic underwater images denoising. In: European Conference on Propagation and Systems, Mar 2005, Brest, France (2005). ffhal-00494314

    Google Scholar 

  7. Bazeille, S., Quidu, I., Jaulin, L., Malkasse, J.-P.: Automatic underwater image pre-processing. In: CMM 2006, Oct 2006, Brest, France (2006). ffhal-00504893

    Google Scholar 

  8. Huang, S., Cheng, F., Chiu, Y.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. 22(3), 1032–1041 (2013). https://doi.org/10.1109/TIP.2012.2226047

    Article  MathSciNet  MATH  Google Scholar 

  9. Abdullah-Al-Wadud, M., Kabir, M.H., Akber Dewan, M.A., Chae, O.: A dynamic histogram equalization for image contrast enhancement. In: IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 593–600 (2007). https://doi.org/10.1109/TCE.2007.381734

  10. Setiawan, A.W., Mengko, T.R., Santoso, O.S., Suksmono, A.B.: Color retinal image enhancement using CLAHE. In: International Conference on ICT for Smart Society, Jakarta, Indonesia, pp. 1–3 (2013). https://doi.org/10.1109/ICTSS.2013.6588092

  11. Ponraj, D.N., Jenifer, M.E., Poongodi, P., Manoharan, J.S.: A survey on the preprocessing techniques of mammogram for the detection of breast cancer. J. Emerg. Trends Comput. Inf. Sci. 2(12), 656–664 (2011)

    Google Scholar 

  12. Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000). https://doi.org/10.1109/83.841534

    Article  Google Scholar 

  13. Zimmerman, J.B., Pizer, S.M., Staab, E.V., Perry, J.R., McCartney, W., Brenton, B.C.: An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Trans. Med. Imaging 7(4), 304–312 (1988). https://doi.org/10.1109/42.14513

    Article  Google Scholar 

  14. Pizer, S.M., Johnston, R.E., Ericksen, J.P., Yankaskas, B.C., Muller, K.E.: Contrast-limited adaptive histogram equalization: speed and effectiveness. In: Proceedings of the First Conference on Visualization in Biomedical Computing, Atlanta, GA, USA, pp. 337–345 (1990). https://doi.org/10.1109/VBC.1990.109340

  15. Hitam, M.S., Awalludin, E.A., Yussof, J.H.W., Bachok, Z.: Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In: 2013 International Conference on Computer Applications Technology (ICCAT), Sousse, Tunisia, pp. 1–5 (2013). https://doi.org/10.1109/ICCAT.2013.6522017

  16. Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Sig. Process.-Syst. Signal Image Video Technol. 38, 35–44 (2004). https://doi.org/10.1023/B:VLSI.0000028532.53893.82

    Article  Google Scholar 

  17. Han, J., Yang, S., Lee, B.: A novel 3-D color histogram equalization method with uniform 1-D gray scale histogram. IEEE Trans. Image Process. 20(2), 506–512 (2011). https://doi.org/10.1109/TIP.2010.2068555

    Article  MathSciNet  MATH  Google Scholar 

  18. Ghani, A.S.A., Isa, N.A.M.: Enhancement of low quality underwater image through integrated global and local contrast correction. Appl. Soft Comput. 37, 332–344 (2015). https://doi.org/10.1016/j.asoc.2015.08.033. ISSN 1568–4946

  19. Iqbal, K., Odetayo, M., James, A., Salam, R.A., Talib, A.Z.H.: Enhancing the low quality images using unsupervised colour correction method. In: 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 1703–1709 (2010). https://doi.org/10.1109/ICSMC.2010.5642311

  20. Schechner, Y.Y., Karpel, N.: Clear underwater vision. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, Washington, DC, USA, 2004, pp. I–I (2004). https://doi.org/10.1109/CVPR.2004.1315078

  21. Henke, B., Vahl, M., Zhou, Z.: Removing color cast of underwater images through non-constant color constancy hypothesis. In: 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), Trieste, Italy, pp. 20–24 (2013). https://doi.org/10.1109/ISPA.2013.6703708

  22. Chikane, V., Fuh, C.-S.: Automatic white balance for digital still cameras. J. Inf. Sci. Eng. 22, 497–509 (2006)

    Google Scholar 

  23. Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27(1), 379–393 (2018). https://doi.org/10.1109/TIP.2017.2759252

    Article  MathSciNet  MATH  Google Scholar 

  24. Foster, D.H.: Does colour constancy exist? Trends in Cognitive Sciences, vol. 7, no. 10, pp. 439–443 (2003). https://doi.org/10.1016/j.tics.2003.08.002. ISSN 1364–6613

  25. Land, E.H.: The Retinex theory of color vision. In: Scientific American, vol. 237, no. 6, pp. 108–129 (1977). www.jstor.org/stable/24953876. Accessed 10 Mar 2021

  26. Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X., Ding, X.: A retinex-based enhancing approach for single underwater image. In: 2014 IEEE International Conference on Image Processing (ICIP), Paris, France, pp. 4572–4576 (2014). https://doi.org/10.1109/ICIP.2014.7025927

  27. Hines, G., Rahman, Z., Jobson, D., Woodell, G.: Single-scale Retinex using digital signal processors (2005). Citeseerx.ist.psu.edu

    Google Scholar 

  28. Xie, B., Guo, F., Cai, Z.: Improved single image Dehazing using dark channel prior and multi-scale retinex. In: 2010 International Conference on Intelligent System Design and Engineering Application, Changsha, China, pp. 848–851 (2010). https://doi.org/10.1109/ISDEA.2010.141

  29. Vishwakarma, A.K., Mishra, A.: Color image enhancement techniques: a critical review. Indian J. Comput. Sci. Eng. 3(1), 39–45 (2012)

    Google Scholar 

  30. Deng, Z., Sun, H., Zhou, S., Zhao, J., Zou, H.: Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(8), 3652–3664 (2017). https://doi.org/10.1109/JSTARS.2017.2694890

    Article  Google Scholar 

  31. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv.org

    Google Scholar 

  32. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91

  33. Fan, Q., Brown, L., Smith, J.: A closer look at faster R-CNN for vehicle detection. In: 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, pp. 124–129 (2016). https://doi.org/10.1109/IVS.2016.7535375

  34. Lan, W., Dang, J., Wang, Y., Wang, S.: Pedestrian detection based on YOLO network model. In: 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Changchun, China, 2018, pp. 1547–1551 (2018). https://doi.org/10.1109/ICMA.2018.8484698

  35. Du, J.: Understanding of object detection based on CNN family and YOLO. In Journal of Physics: Conference Series, vol. 1004, no. 1, p. 012029 (2018)

    Google Scholar 

  36. Zhu, H., Meng, F., Cai, J., Lu, S.: Beyond pixels: a comprehensive survey from bottom-up to semantic image segmentation and cosegmentation. J. Vis. Commun. Image Represent. 34, 12–27 (2016). https://doi.org/10.1016/j.jvcir.2015.10.012. (ISSN 1047–3203)

  37. Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: Full-resolution residual networks for semantic segmentation in street scenes. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 3309–3318 (2017). https://doi.org/10.1109/CVPR.2017.353

  38. Romera-Paredes, B., Torr, P.H.S.: Recurrent instance segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. LNCS, vol. 9910, pp. 312–329. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_19

    Chapter  Google Scholar 

  39. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2961–2969 (2017)

    Google Scholar 

  40. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1520–1528 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Craig Pirie & Carlos Francisco Moreno-Garcia

Authors
  1. Craig Pirie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Francisco Moreno-Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Francisco Moreno-Garcia .

Editor information

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

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

Publish with us

Policies and ethics