Optimized AI methods for rapid crack detection in microscopy images

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dc.contributor.authorLou, Chenxukun
dc.contributor.authorTinsley, Lawrence
dc.contributor.authorDuarte Martinez, Fabian
dc.contributor.authorGray, Simon
dc.contributor.authorHonarvar Shakibaei Asli, Barmak
dc.date.accessioned2025-01-09T15:02:53Z
dc.date.available2025-01-09T15:02:53Z
dc.date.freetoread2025-01-09
dc.date.issued2024-12-06
dc.date.pubOnline2024-12-06
dc.description.abstractDetecting structural cracks is critical for quality control and maintenance of industrial materials, ensuring their safety and extending service life. This study enhances the automation and accuracy of crack detection in microscopic images using advanced image processing and deep learning techniques, particularly the YOLOv8 model. A comprehensive review of relevant literature was carried out to compare traditional image-processing methods with modern machine-learning approaches. The YOLOv8 model was optimized by incorporating the Wise Intersection over Union (WIoU) loss function and the bidirectional feature pyramid network (BiFPN) technique, achieving precise detection results with mean average precision (mAP@0.5) of 0.895 and a precision rate of 0.859, demonstrating its superiority in detecting fine cracks even in complex and noisy backgrounds. Experimental findings confirmed the model’s high accuracy in identifying cracks, even under challenging conditions. Despite these advancements, detecting very small or overlapping cracks in complex backgrounds remains challenging. Our future work will focus on optimizing and extending the model’s generalisation capabilities. The findings of this study provide a solid foundation for automatic and rapid crack detection in industrial applications and indicate potential for broader applications across various fields.
dc.description.journalNameElectronics
dc.identifier.citationLou C, Tinsley L, Duarte Martinez F, et al., (2024) Optimized AI methods for rapid crack detection in microscopy images. Electronics, Volume 13, Issue 23, Article number 4824
dc.identifier.eissn2079-9292
dc.identifier.elementsID560101
dc.identifier.issn1450-5843
dc.identifier.issueNo23
dc.identifier.paperNo4824
dc.identifier.urihttps://doi.org/10.3390/electronics13234824
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23341
dc.identifier.volumeNo13
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2079-9292/13/23/4824
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject40 Engineering
dc.subject4009 Electronics, Sensors and Digital Hardware
dc.subjectMachine Learning and Artificial Intelligence
dc.subject4009 Electronics, sensors and digital hardware
dc.subjectcrack detection
dc.subjectYOLOv8
dc.subjectdeep learning
dc.subjectmicroscopic images
dc.subjectstructural integrity
dc.titleOptimized AI methods for rapid crack detection in microscopy images
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-12-03

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