Development of deep learning algorithm using yolov8 to detect crack on concrete surfaces

Publication:
Development of deep learning algorithm using yolov8 to detect crack on concrete surfaces

datacite.subject.fos	oecd::Engineering and technology::Mechanical engineering
dc.contributor.author	Jeremy, Choy Jun Min
dc.date.accessioned	2026-01-15T08:23:52Z
dc.date.available	2026-01-15T08:23:52Z
dc.date.issued	2023-06-19
dc.description.abstract	Crack detection plays a crucial role in infrastructure maintenance and safety. Traditional methods for crack detection rely on manual visual inspection which is time-consuming and labor-intensive. In recent year, deep learning techniques have shown promising results in automating crack detection. This paper presents the development of a deep learning algorithm to perform crack detection and segmentation on concrete crack surfaces. There are two models developed which achieve mAP of 79% and 74% respectively, tuned with different hyperparameters. Both models are deployed on multiple platforms including Windows, macOS and Android.
dc.identifier.uri	https://erepo.usm.my/handle/123456789/23455
dc.language.iso	en
dc.title	Development of deep learning algorithm using yolov8 to detect crack on concrete surfaces
dc.type	Resource Types::text::report::technical report
dspace.entity.type	Publication
oairecerif.author.affiliation	Universiti Sains Malaysia