Abstract
According to World Corrosion Organization (WCO), the estimated annual cost of damage due to corrosion across the globe is approximately US$2.5 trillion which contributes 3–4% GDP of developed countries. Minimizing losses due to corrosion and to ensure longer life of structures is thus a major concern. Paper presents a technique employing bag of features (BoF) for underwater structural corrosion recognition. BoF methods are based on an unorganized grouping of image features and it is conceptually simpler than various other alternatives. The model is trained on three labeled datasets corroded, un-corroded, and damaged obtained from underwater structures along the Gomti River in Lucknow. Dataset of around 2000 images is used to train the model. Trained prototype BoF learning model is capable of efficiently classifying pure and corroded images and achieves an accuracy of 82.38% demonstrating the feasibility of this method. The technique proposed and its deployment on handheld and autonomous devices provide an efficient and intelligent method for underwater structure corrosion recognition.
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References
C. I. Committee 546, Guide to Underwater Repair of Concrete, America Concrete Institute, Colo, USA, 2006
Lasa RP, Kessler R (1997) Practical application of cathodic protection systems for reinforcing steel substructures in marine environment. In: Proceedings of the international seminar on repair and rehabilitation of reinforced concrete structures, pp 16–31, Maracaibo, Venezuela, May 1997
Farid Uddin KM, Ohtsu M, Hossain KMA, Lachemi M (2007) Simulation of reinforcement–corrosion induced crack propagation in concrete by acoustic emission technique and boundary element method analysis. Canadian J Civ Eng 34:1197–1207
Sun X, Conglian Z (2011) Casing corrosion prediction based on grey support vector machine. In: 2011 International conference on electric information and control engineering, pp 4831–4834
Wu X, Ren J, Ye Y, Ren H (2016) Prediction of the laws of carbon steel erosion corrosion in sour water system based on decision tree and two kinds of artificial neural network model. In: 2016 Chinese control and decision conference (CCDC), pp 3872–3876
Bondada V, Pratihar DK, International conference on robotics and smart manufacturing, Elsevier
Naladala I, Raju A (2018) Corrosion damage identification and lifetime estimation of ship parts using image processing. In: International conference on advances in computing, communications and informatics (ICACCI)
Diaz JAI, Ligeralde Jr MI (2017) Rust detection using image processing via Matlab. In: TENCON 5z2017—2017 IEEE region 10 conference
Shi P, Fan X (2016) A detection and classification approach for underwater dam cracks. Sage
Bonnín-Pascual F, Rodriguez AO (2014) ‘Corrosion detection for automated visual inspection. In: Developments in corrosion protection. InTech, Rijeka, Croatia (2014)
Pan B, Qian K, Xie H, Asundi A (2009) Two-dimensional digital image correlation for in-plane displacement and strain measurement: a review. Measure Sci Technol 20(6):27
Singh PK, Kar AK, Singh Y, Kolekar MH, Tanwar S (eds) (2019) Proceedings of ICRIC 2019: recent innovations in computing, vol 597. Springer Nature, Berlin
Wang Y, Chin (2017) A deep CNN method for underwater image enhancement. In: IEEE international conference on image processing, (ICIP)
Baba T, Nakamura K (2017) Image enhancement method for underwater images based on discrete cosine eigenbasis transformation. In: IEEE international conference on image processing, (ICIP)
Tanwar S (2020) Fog data analytics for IoT applications—next generation process model with state-of-the-art technologies, studies in big data. Springer International Publishing, Berlin, pp 1–550
Singh PK, Pawłowski W, Tanwar S, Kumar N, Rodrigues JJ, Obaidat MS (eds) (2020) Proceedings of first international conference on computing, communications, and cyber-security (IC4S 2019), vol 121. Springer, Berlin
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Sinha, A., Kumar, S., Khanna, P., Pragya (2021). Corrosion Estimation of Underwater Structures Employing Bag of Features (BoF). In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-0733-2_72
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DOI: https://doi.org/10.1007/978-981-16-0733-2_72
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