Real-Time Detection and Tracking of Defects in Building Based on Augmented Reality and Computer Vision

Wenyu Xu, Yi Tan () and Shenghan Li
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Wenyu Xu: The Shenzhen University
Yi Tan: The Shenzhen University
Shenghan Li: The Shenzhen University

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 1638-1650 from Springer

Abstract: Abstract Condition assessment and health monitoring (CAHM) of buildings require effective and continuous detection of any changes in the material and geometric properties of components to detect defects in time. However, traditional manual-based detection methods are inefficient and error-prone. Smartphone/tablet-based detection has achieved real-time detection of the CAHM with improved efficiency, however inspectors still need to hold the smart devices in hands, resulting in inconveniency and uncomfortable working experience. In this study, a head mounted display (HMD)-based collaborative method for real-time detection and tracking of defects (i.e., crack, swell, peel, seepage, and mould) in building was developed by combining an object detection algorithm you only look once version 5 (YOLOv5) with multi-object tracking algorithm Deepsort. According to the analysis of the experimental results, the developed method is promising and efficient to detect and track various types of building defects.

Keywords: Augmented reality; Computer vision; Defects; Detection and tracking (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_126

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