nach oben

Erschienen in:

2023 | OriginalPaper | Buchkapitel

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

verfasst von : Wenyu xu, Yi Tan, Shenghan Li

Erschienen in: Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Conceptualizing Community Participation in the Context of Megaprojects-Induced Internal Displacement

Nächstes Kapitel Precise Urban Green Volume-Enabled Building and Environment Simulation: Sub-meter Voxel Modeling of Airborne and Hand-Held 3D Scans of Urban Trees

Shruthy, P., Gurmu, A.: Framework for building defects and their identification technologies: case studies of domestic buildings in Melbourne, Australia. In: Proceedings of the 54th International Conference of the Architectural Science Association, pp. 1–10 (2021)

Xu, Z., Li, S., Li, H., Li, Q.: Modeling and problem solving of building defects using point clouds and enhanced case-based reasoning. Autom. Constr. 96, 40–54 (2018). https://doi.org/10.1016/j.autcon.2018.09.003CrossRef

Hopkin, T., Lu, S.-L., Sexton, M., Rogers, P.: Learning from defects in the UK housing sector using action research. Eng. Constr. Archit. Manag. 26(8), 1608–1624 (2019). https://doi.org/10.1108/ecam-04-2018-0146CrossRef

Mohseni, H., Setunge, S., Zhang, G.M., Wakefield, R.: Condition monitoring and condition aggregation for optimised decision making in management of buildings. Appl. Mech. Mater. 438–439 (2013)

Shamshirband, S., Mosavi, A., Rabczuk, T.: Particle swarm optimization model to predict scour depth around bridge pier (2019). https://doi.org/10.20944/preprints201905.0121.v1

Noel, A.B., Abdaoui, A., Elfouly, T., Ahmed, M.H., Badawy, A., Shehata, M.S.: Structural health monitoring using wireless sensor networks: a comprehensive survey. IEEE Commun. Surv. Tutorials 19(3), 1403–1423 (2017). https://doi.org/10.1109/comst.2017.2691551CrossRef

Kong, Q., Allen, R.M., Kohler, M.D., Heaton, T.H., Bunn, J.: Structural health monitoring of buildings using smartphone sensors. Seismol. Res. Lett. 89(2A), 594–602 (2018)CrossRef

Kumar, P., Batchu, S., Swamy, S., N., and Kota, S. R.: Real-time concrete damage detection using deep learning for high rise structures. IEEE Access 9, 112312–112331 (2021). https://doi.org/10.1109/access.2021.3102647CrossRef

Jiang, Y., Pang, D., and Li, C.: A deep learning approach for fast detection and classification of concrete damage. Autom. Constr. 128, 103785 (2021). https://doi.org/10.1016/j.autcon.2021.103785

10.

Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.P.: Deep learning for image-based cassava disease detection, Front. Plant. Sci. 8, 1852 (2017). https://www.ncbi.nlm.nih.gov/pubmed/29163582

11.

Christodoulou, S.E., Kyriakou, C., Hadjidemetriou, G.: Pavement patch defects detection and classification using smartphones vibration signals and video images. In: Mobility Patterns, Big Data and Transport Analytics, pp. 365–3802019https://doi.org/10.1016/B978-0-12-812970-8.00014-2

12.

Perez, H., Tah, J.H.M.: Deep learning smartphone application for real‐time detection of defects in buildings. Struct. Contr. Health Monit. 28(7), e2751 (2021). https://doi.org/10.1002/stc.2751

13.

Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP) (2016)

14.

Kalman, R.E.: New approach to linear filtering and prediction problems transaction of the ASME. J. Basic Eng. 83, 95–108 (1960)

15.

Maesschalck, R.D., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemometr. Intellig. Lab. Syst. 50(1), 1–18 (2000)CrossRef

16.

Ge, Y., et al.: Tracking and counting of tomato at different growth period using an improving YOLO-deepsort network for inspection robot. Machines 10(6), 489 (2022). https://doi.org/10.3390/machines10060489

17.

Wright, M.B.: Speeding up the Hungarian algorithm. Comput. Oper. Res 17, 95–96 (1990)CrossRef

18.

Modarres, C., Astorga, N, Droguett, E.L., Meruane, V.: Convolutional neural networks for automated damage recognition and damage type identification. Struct. Control Health Monit. 25(10), e2230 (2018)

19.

Tang, Z., Chen, Z., Bao, Y., Li, H.: Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct. Control Health Monit. 26(1), e2296 (2019). https://doi.org/10.1002/stc.2296

Titel: Real-Time Detection and Tracking of Defects in Building Based on Augmented Reality and Computer Vision
verfasst von: Wenyu xu
Yi Tan
Shenghan Li
Verlag: Springer Nature Singapore
Buch: Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate
Print ISBN: 978-981-9936-25-0

Electronic ISBN: 978-981-9936-26-7

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-981-99-3626-7_126

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"