Abstract
Corrosion—degradation in metal structures—is problematic, expensive to rectify, and can be unpredictable in the rate at which it spreads. Traditional preventative maintenance techniques are complemented by human visual inspection, in turn complemented by artificial intelligence vision techniques. The primary objective of this paper was to determine the most accurate deep learning model for use in corrosion detection; to achieve this, we devised an experimental comparison that tested five machine learning algorithms for the detection of corrosion from image data. The deep learning that forms the basis of algorithms used to solve object recognition problems traditionally requires large amounts of training data. As this data requires manual labelling by a person who is expert in the domain of corrosion, it is difficult and expensive to obtain; time and expense that increase considerably as more sophisticated pixel-level annotation is applied. We discovered that high levels of accuracy (98%) can be achieved using deep learning to detect corrosion using samples annotated with simple, image-level labels. We achieved this headline accuracy through the application of transfer learning using models that had been trained on the ImageNet dataset. With many deep learning algorithms to choose from, we systematically determined the most accurate model to use as a basis for further experimentation.
Supported by the University of Salford and Add Energy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hinton, G.E., Krizhevsky, A., Sutskever, I.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Liu, Y., Dhillon, B.S.: Human error in maintenance: a review. J. Quality Maint. Eng. 12, 21–36 (2006)
Kelly, R., Shaw, B.: What is Corrosion?. Electrochem. Soc. Interf. (2006)
Bastian, B. et al.: Visual inspection and characterization of external corrosion in pipelines using deep neural network. NDTE Int. 107 (2019)
Chen, L-C. et al.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV (2018)
Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Gopalakrishnan, K., et al.: Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr. Build. Mater. 157, 322–330 (2017)
Guo, Y., et al.: Deep learning for visual understanding: a review. Neurocomputing 187, 27–48 (2015)
Hashemian, H.M.: State-of-the-art predictive maintenance techniques. IEEE Trans. Instrum. Meas. 60(1), 226–236 (2011)
He, K. et al.: Mask R-CNN. Comput. Vision Pattern Recogn. (2017). https://arxiv.org/abs/1703.06870
Huang, Y., et al.: Cost-effective vehicle type recognition in surveillance images with deep active learning and web data. IEEE Trans. Intell. Transport. Syst. 21(1), 79–86 (2020)
ImageNet. Web Page (2021)
Kingma, D.P., Adam, J.B.: A method for stochastic optimization. Conference Paper (2015) https://arxiv.org/abs/1412.6980
Liu, Y., Yeoh, J.K.W.: Robust pixel-wise concrete crack segmentation and properties retrieval using image patches. Autom. Constr. 123, 103535 (2021)
Norouzzadeh, M.S., et al.: A deep active learning system for species identification and counting in camera trap images. Methods Ecol. Evol. 12(1), 150–161 (2020)
Gangsar, P., Tiwari, R.: Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of- the-art review. Mech. Syst. Signal Process. 144, 106908 (2020)
See, J.E.: Visual inspection reliability for precision manufactured parts. Human Fact. 57(8) (2015)
Singla, A., Bertino, E., Verma, D.: Overcoming the lack of labeled data: training intrusion detection models using transfer learning. In: 2019 IEEE International Conference on Smart Computing (SMART- COMP) (2019)
Pitts, W.M.C.W.: How we know universals the perception of auditory and visual forms. Bull. Math. Biophys. 9, 127–147 (1947)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3 (2016)
Wu, X., et al.: COVID-AL: The diagnosis of COVID-19 with deep active learning. Medical Image Analysis 68, 101913 (2021)
Yu, L., et al.: AMCD: an accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection. J. Ambient Intell. Human. Comput. (2021)
Acknowledgements
We would like to thank Dr. Blossom Bastian [4] for providing the dataset used in these experiments. This research is supported by the University of Salford and Add Energy.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bolton, T., Bass, J., Gaber, T. (2023). A Comparison of Deep Learning Techniques for Corrosion Detection. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-20601-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20600-9
Online ISBN: 978-3-031-20601-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)