Application of CNN for multiple phase corrosion identification and region detection

dc.contributor.authorOyedeji, Oluseyi Ayodeji
dc.contributor.authorKhan, Samir
dc.contributor.authorErkoyuncu, John Ahmet
dc.date.accessioned2024-08-29T12:00:26Z
dc.date.available2024-08-29T12:00:26Z
dc.date.freetoread2024-08-29
dc.date.issued2024-10-30
dc.date.pubOnline2024-07-23
dc.description.abstractCorrosion is a significant issue that contributes negatively to the degradation of materials most especially metals. To ensure proper maintenance, enhance reliability and prevent breakdown, it is very essential to not only effectively detect corrosion but to also understand its locations and distributions on the materials. A Multiple phase Convolutional Neural Network (CNN) model is created for this purpose. The Multiple phase CNN model consists of custom designed deep learning algorithms at various stages. This created the opportunity to make use of binary classification, multi-label classification and patch distribution algorithm to detect and identify corrosion regions on metallic materials. Six (6) different labels of corrosion were modelled to represent different levels of degradation using 600 anonymized images. The images were used in the various stages of the framework for training the respective models. Results at the binary level shows 94.87 % of corrosion detection. The multiclass stage of the Multiple phase CNN records the highest accuracy of 92.1 %. The patch distribution stage recorded a highest accuracy of 96.5 % and 94.6 % for the Average Image and Average Pixel ROCAUC (Region of Concentration Area Under Cover). It also shows a region segment average accuracy detection of 91.5 % (image level) and 89.2 %(pixel level) for 9 distinct regions. The research provides a comprehensive and detailed reliability and maintenance information for Aerospace, Transport and Manufacturing Materials experts and non-experts. The framework shows a robust approach to detecting corrosion which is essential for critical and safety applications as well as preventing economic loss due to corrosion. This can also be extended to other domains beyond the corrosion case study.
dc.description.journalNameApplied Soft Computing
dc.identifier.citationOyedeji OA, Khan S, Erkoyuncu JA. (2024) Application of CNN for multiple phase corrosion identification and region detection. Applied Soft Computing, Volume 164, October 2024, Article number 112008
dc.identifier.eissn1872-9681
dc.identifier.elementsID549567
dc.identifier.issn1568-4946
dc.identifier.paperNo112008
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2024.112008
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22847
dc.identifier.volumeNo164
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier BV
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S1568494624007828?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCorrosion
dc.subjectConvolutional neural network (CNN)
dc.subjectReliability
dc.subjectMaintenance
dc.subjectDegradation
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subjectPrevention
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectArtificial Intelligence & Image Processing
dc.subject4602 Artificial intelligence
dc.subject4903 Numerical and computational mathematics
dc.titleApplication of CNN for multiple phase corrosion identification and region detection
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-07-06

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