Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision

Jinxin Wang; Shenglei Zhao; Limin Shen; Wenxian Yang; Jinxin Ma

doi:10.1007/978-3-031-26193-0_98

Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision

Jinxin Wang, Shenglei Zhao, Limin Shen, Wenxian Yang, Jinxin Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Old residential areas have long been a weak link in urban management, and building surface defects frequently occur, seriously endangering social and public safety. As a typical representative of building surface defects, building surface cracks are one of the main factors affecting building safety. This paper proposes a crack detection method for old residential buildings based on unmanned aerial vehicle (UAV) intelligent vision, which mainly includes image preprocessing and surface defect image recognition. First, median filter preprocessing is performed on the image data with and without cracks collected by the UAV. Next, to improve the robustness of the model, data augmentation is performed on the preprocessed image data. Finally, a Convolutional Neural Network (CNN) is established to realize the surface recognition of defect images. Confusion matrix evaluation and confidence test results demonstrate the feasibility and effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings of TEPEN 2022
Subtitle of host publication	Efficiency and Performance Engineering Network
Editors	Hao Zhang, Yongjian Ji, Tongtong Liu, Xiuquan Sun, Andrew David Ball
Publisher	Springer, Cham
Pages	1126-1135
Number of pages	10
Edition	1st
ISBN (Electronic)	9783031261930
ISBN (Print)	9783031261923, 9783031261954
DOIs	https://doi.org/10.1007/978-3-031-26193-0_98
Publication status	Published - 4 Mar 2023
Externally published	Yes
Event	International Conference of The Efficiency and Performance Engineering Network 2022 - Baotou, China Duration: 18 Aug 2022 → 21 Aug 2022 https://tepen.net/ https://tepen.net/conference/tepen2022/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer Cham
Volume	129 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	International Conference of The Efficiency and Performance Engineering Network 2022
Abbreviated title	TEPEN 2022
Country/Territory	China
City	Baotou
Period	18/08/22 → 21/08/22
Internet address	https://tepen.net/ https://tepen.net/conference/tepen2022/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-26193-0_98Licence: Unspecified

Cite this

Wang, J., Zhao, S., Shen, L., Yang, W., & Ma, J. (2023). Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision. In H. Zhang, Y. Ji, T. Liu, X. Sun, & A. D. Ball (Eds.), Proceedings of TEPEN 2022: Efficiency and Performance Engineering Network (1st ed., pp. 1126-1135). (Mechanisms and Machine Science; Vol. 129 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-26193-0_98

@inproceedings{1b25ee7e864f4d3dbe3147b4a71ec1a6,

title = "Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision",

abstract = "Old residential areas have long been a weak link in urban management, and building surface defects frequently occur, seriously endangering social and public safety. As a typical representative of building surface defects, building surface cracks are one of the main factors affecting building safety. This paper proposes a crack detection method for old residential buildings based on unmanned aerial vehicle (UAV) intelligent vision, which mainly includes image preprocessing and surface defect image recognition. First, median filter preprocessing is performed on the image data with and without cracks collected by the UAV. Next, to improve the robustness of the model, data augmentation is performed on the preprocessed image data. Finally, a Convolutional Neural Network (CNN) is established to realize the surface recognition of defect images. Confusion matrix evaluation and confidence test results demonstrate the feasibility and effectiveness of the proposed method.",

keywords = "Convolutional Neural Network (CNN), Crack detection, Intelligent vision, Median filter preprocessing, Surface defects",

author = "Jinxin Wang and Shenglei Zhao and Limin Shen and Wenxian Yang and Jinxin Ma",

note = "Funding Information: Acknowledgments. This work was supported by the Fundamental Research Funds for the Central Universities (Grant No: 2021QN1089), and the Efficiency and performance Engineering Network International Collaboration Fund (TEPEN-ICF2021-05). Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference of The Efficiency and Performance Engineering Network 2022, TEPEN 2022 ; Conference date: 18-08-2022 Through 21-08-2022",

year = "2023",

month = mar,

day = "4",

doi = "10.1007/978-3-031-26193-0_98",

language = "English",

isbn = "9783031261923",

series = "Mechanisms and Machine Science",

publisher = "Springer, Cham",

pages = "1126--1135",

editor = "Hao Zhang and Yongjian Ji and Tongtong Liu and Xiuquan Sun and Ball, {Andrew David}",

booktitle = "Proceedings of TEPEN 2022",

address = "Switzerland",

edition = "1st",

url = "https://tepen.net/, https://tepen.net/conference/tepen2022/",

}

Wang, J, Zhao, S, Shen, L, Yang, W & Ma, J 2023, Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision. in H Zhang, Y Ji, T Liu, X Sun & AD Ball (eds), Proceedings of TEPEN 2022: Efficiency and Performance Engineering Network. 1st edn, Mechanisms and Machine Science, vol. 129 MMS, Springer, Cham, pp. 1126-1135, International Conference of The Efficiency and Performance Engineering Network 2022, Baotou, China, 18/08/22. https://doi.org/10.1007/978-3-031-26193-0_98

Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision. / Wang, Jinxin; Zhao, Shenglei; Shen, Limin et al.

Proceedings of TEPEN 2022: Efficiency and Performance Engineering Network. ed. / Hao Zhang; Yongjian Ji; Tongtong Liu; Xiuquan Sun; Andrew David Ball. 1st. ed. Springer, Cham, 2023. p. 1126-1135 (Mechanisms and Machine Science; Vol. 129 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision

AU - Wang, Jinxin

AU - Zhao, Shenglei

AU - Shen, Limin

AU - Yang, Wenxian

AU - Ma, Jinxin

N1 - Funding Information: Acknowledgments. This work was supported by the Fundamental Research Funds for the Central Universities (Grant No: 2021QN1089), and the Efficiency and performance Engineering Network International Collaboration Fund (TEPEN-ICF2021-05). Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023/3/4

Y1 - 2023/3/4

N2 - Old residential areas have long been a weak link in urban management, and building surface defects frequently occur, seriously endangering social and public safety. As a typical representative of building surface defects, building surface cracks are one of the main factors affecting building safety. This paper proposes a crack detection method for old residential buildings based on unmanned aerial vehicle (UAV) intelligent vision, which mainly includes image preprocessing and surface defect image recognition. First, median filter preprocessing is performed on the image data with and without cracks collected by the UAV. Next, to improve the robustness of the model, data augmentation is performed on the preprocessed image data. Finally, a Convolutional Neural Network (CNN) is established to realize the surface recognition of defect images. Confusion matrix evaluation and confidence test results demonstrate the feasibility and effectiveness of the proposed method.

AB - Old residential areas have long been a weak link in urban management, and building surface defects frequently occur, seriously endangering social and public safety. As a typical representative of building surface defects, building surface cracks are one of the main factors affecting building safety. This paper proposes a crack detection method for old residential buildings based on unmanned aerial vehicle (UAV) intelligent vision, which mainly includes image preprocessing and surface defect image recognition. First, median filter preprocessing is performed on the image data with and without cracks collected by the UAV. Next, to improve the robustness of the model, data augmentation is performed on the preprocessed image data. Finally, a Convolutional Neural Network (CNN) is established to realize the surface recognition of defect images. Confusion matrix evaluation and confidence test results demonstrate the feasibility and effectiveness of the proposed method.

KW - Convolutional Neural Network (CNN)

KW - Crack detection

KW - Intelligent vision

KW - Median filter preprocessing

KW - Surface defects

UR - http://www.scopus.com/inward/record.url?scp=85151130402&partnerID=8YFLogxK

UR - https://link.springer.com/book/10.1007/978-3-031-26193-0

U2 - 10.1007/978-3-031-26193-0_98

DO - 10.1007/978-3-031-26193-0_98

M3 - Conference contribution

AN - SCOPUS:85151130402

SN - 9783031261923

SN - 9783031261954

T3 - Mechanisms and Machine Science

SP - 1126

EP - 1135

BT - Proceedings of TEPEN 2022

A2 - Zhang, Hao

A2 - Ji, Yongjian

A2 - Liu, Tongtong

A2 - Sun, Xiuquan

A2 - Ball, Andrew David

PB - Springer, Cham

T2 - International Conference of The Efficiency and Performance Engineering Network 2022

Y2 - 18 August 2022 through 21 August 2022

ER -

Crack Detection of Old Residential Buildings Based on UAV Intelligent Vision

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Cite this