Improving Oil Pipeline Surveillance with a Novel 3D Drone Simulation Using Dynamically Constrained Accumulative Membership Fuzzy Logic Algorithm (DCAMFL) for Crack Detection

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dc.contributor.authorMuhi, Omar Saber
dc.contributor.authorFarhan, Hameed Mutlag
dc.contributor.authorKurnaz, Sefer
dc.date.accessioned2025-08-02T06:19:37Z
dc.date.available2025-08-02T06:19:37Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractCracks in oil pipelines pose significant risks to the environment, public safety, and the overall integrity of the infrastructure. In this paper, we propose a novel approach for crack detection in oil pipes using a combination of 3D drone simulation, convolutional neural network (CNN) feature extraction, and the dynamically constrained accumulative membership fuzzy logic algorithm (DCAMFL). The algorithm leverages the strengths of CNNs in extracting discriminative features from images and the DCAMFL’s ability to handle uncertainties and overlapping linguistic variables. We evaluated the proposed algorithm on a comprehensive dataset containing images of cracked oil pipes, achieving remarkable results. The precision, recall, and F1-score for crack detection were found to be 96.5%, 97.3%, and 95.6%, respectively. These high-performance metrics demonstrate the algorithm’s accuracy and reliability in identifying and classifying cracks. Our findings highlight the effectiveness of integrating advanced simulation techniques, deep learning, and fuzzy logic for crack detection in oil pipelines. The proposed algorithm holds promise for enhancing pipeline surveillance, improving safety measures, and extending the lifespan of oil infrastructure. Future work involves expanding the dataset, fine-tuning the CNN architecture, and validating the algorithm on large-scale pipelines to further enhance its performance and applicability.
dc.identifier.citationMuhi, O. S., Farhan, H. M., & Kurnaz, S. (2025). Improving Oil Pipeline Surveillance with a Novel 3D Drone Simulation Using Dynamically Constrained Accumulative Membership Fuzzy Logic Algorithm (DCAMFL) for Crack Detection. International Journal of Computational Intelligence Systems, 18(1), 114. 10.1007/s44196-025-00818-3
dc.identifier.doi10.1007/s44196-025-00818-3
dc.identifier.issn1875-6891
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105004743194
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5817
dc.identifier.volume18
dc.identifier.wosWOS:001486294700005
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorMuhi, Omar Saber
dc.institutionauthorFarhan, Hameed Mutlag
dc.institutionauthorKurnaz, Sefer
dc.language.isoen
dc.publisherSpringer Science and Business Media B.V.
dc.relation.ispartofInternational Journal of Computational Intelligence Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subject3D drone simulation
dc.subjectCNN
dc.subjectCrack detection
dc.subjectFuzzy logic
dc.subjectOil pipelines
dc.titleImproving Oil Pipeline Surveillance with a Novel 3D Drone Simulation Using Dynamically Constrained Accumulative Membership Fuzzy Logic Algorithm (DCAMFL) for Crack Detection
dc.typeArticle

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