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J. Saf. Secur. Eng."},{"key":"ref2","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.procs.2018.07.115","article-title":"Detection and quantitative assessment of corrosion on pipelines through image analysis","volume":"133","author":"Bondada","year":"Jan. 2018","journal-title":"Procedia Comput. Sci."},{"key":"ref3","doi-asserted-by":"crossref","DOI":"10.1016\/j.apor.2019.05.008","article-title":"Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network","volume":"90","author":"Yao","year":"Sep. 2019, Art. no. 101823","journal-title":"Appl. Ocean Res."},{"key":"ref4","article-title":"Visual inspection and characterization of external corrosion in pipelines using deep neural network","volume":"107","author":"Bastian","year":"Oct. 2019, Art. no. 102134","journal-title":"NDT & E Int."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/6765274","article-title":"Image processing-based pitting corrosion detection using metaheuristic optimized multilevel image thresholding and machine-learning approaches","volume":"2020","author":"Hoang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jmst.2020.01.044","article-title":"An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels","volume":"49","author":"Zhi","year":"Jul. 2020","journal-title":"J. Mater Sci Technol."},{"key":"ref7","volume":"12509","author":"Katsamenis","year":"Nov. 5\u20137, 2020","journal-title":"Lecture Notes in Computer Science"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1201\/9781003230373-34","author":"Matthaiou","year":"Dec. 2021","journal-title":"Developments in the Analysis and Design of Marine Structures"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1007\/978-3-030-77980-1_25","article-title":"Corrosion detection on aircraft fuselage with multi-teacher knowledge distillation","volume":"12747","author":"Zuchniak","year":"2021","journal-title":"Lecture Notes in Computer Science"},{"key":"ref10","doi-asserted-by":"crossref","DOI":"10.3390\/s21124026","article-title":"Aircraft fuselage corrosion detection using Artificial Intelligence","volume":"21","author":"Brandoli","year":"Jun. 2021, Art. no. 4026","journal-title":"Sensors"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"8087","DOI":"10.1007\/s12652-021-03580-4","article-title":"AMCD: An accurate deep learning-based metallic corrosion detector for MAV-based real-time visual inspection","volume":"14","author":"Yu","year":"2023","journal-title":"J. Ambient Intell. Humaniz Comput."},{"key":"ref12","article-title":"Optimized deep learning framework for detecting pitting corrosion based on image segmentation","volume":"17","author":"Ahuja","year":"Jul. 2021","journal-title":"Int. J. Perform. Eng."},{"key":"ref13","article-title":"CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning","volume":"193","author":"Forkan","year":"Feb. 2021, Art. no. 116461","journal-title":"Expert. Syst. Appl."},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.3390\/buildings12020156","article-title":"Civil infrastructure damage and corrosion detection: An application of machine learning","volume":"12","author":"Munawar","year":"Feb. 2022","journal-title":"Buildings"},{"key":"ref15","first-page":"1","article-title":"Deep learning corrosion detection with confidence","volume":"6","author":"Nash","year":"Mar. 2022","journal-title":"npj Mat. Degrad."},{"key":"ref16","unstructured":"B. Burton, W. T. 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Sci."},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.3389\/fbuil.2023.1144606","article-title":"Damage detection on steel-reinforced concrete produced by corrosion via YOLOv3: A detailed guide","volume":"9","author":"Guzm\u00e1n-Torres","year":"Mar. 2023","journal-title":"Front. Built Environ."},{"key":"ref20","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.psep.2023.04.010","article-title":"Predictive deep learning for pitting corrosion modeling in buried transmission pipelines","volume":"174","author":"Akhlaghi","year":"Jun. 2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref21","article-title":"Automated corrosion detection on steel structures using convolutional neural network","volume":"7","author":"Effendi","year":"Mar. 2023","journal-title":"Int. J. Mech. Eng. Sci."},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1155\/2024\/1898088","article-title":"DSNet: A computer vision-based detection and corrosion segmentation network for corroded bolt detection in tunnel","volume":"2024","author":"Tan","year":"2024","journal-title":"Struct. Control Health Monit."},{"key":"ref23","series-title":"Proc ADICS","first-page":"1","article-title":"YOLOv8: A novel object detection algorithm with enhanced performance and robustness","author":"Varghese","year":"2024"},{"key":"ref24","doi-asserted-by":"crossref","DOI":"10.3390\/electronics12081913","article-title":"Voting-Based Deep Convolutional Neural Networks (VB-DCNNs) for M-QAM and M-PSK signals classification","volume":"12","author":"Talha","year":"2023, Art. no. 1913","journal-title":"Electronics"},{"key":"ref25","series-title":"Proc. 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