DETECTING BUILDING DEFECTS WITH DEEP LEARNING
Mudabbir M. Mosavi A. Perez H.
2025L.N. Gumilyov Eurasian National University
Eurasian Journal of Mathematical and Computer Applications
2025#13Issue 350 - 67 pp.
Building defects on external walls can include cracks, mould, dampness from waterproofing failures, fungus growth due to high humidity, and paint peeling. These building defects are commonly caused by wear and tear, improper maintenance, and weather conditions. The identification of these defects is very important to maintain the structural health and safety of buildings, which are often a large financial asset. Manual visual inspection is a traditional technique for defect detection and the most laborious way to identify wear defects, in addition to other nondestructive testing procedures that determine defect properties. Advances in DL and computer vision are expected to improve the efficiency of defect detection. For instance, the DL-based YOLOv10 (You Only Look Once) method provides real-time defect detection that is fast and accurate. This study provided the YOLOv10 technique for the automated detection and localization of building defects. In addition, this study not only makes defect detection more efficient but also helps researchers to advance the overall inspection process with more efficiency.
68-XX , applied AI , applied mathematics , architecture , artificial intelligence , big data , building health monitoring , data science , deep learning , machine learning , structural analysis
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John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
Ludovika University of Public Service, Budapest, Hungary
Abylkas Saginov Karaganda Technical University, Karaganda, Kazakhstan
Univerzita J. Selyeho Komarom, Slovakia
School of the Built Environment, Oxford Brookes University, Oxford, United Kingdom
John von Neumann Faculty of Informatics
Ludovika University of Public Service
Abylkas Saginov Karaganda Technical University
Univerzita J. Selyeho Komarom
School of the Built Environment
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