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Author:

Sun, Guangmin (Sun, Guangmin.) (Scholars:孙光民) | Chen, Jiayang (Chen, Jiayang.) | Li, Bing (Li, Bing.) | Li, Yu (Li, Yu.) | Yan, Dong (Yan, Dong.)

Indexed by:

EI Scopus CSCD

Abstract:

To facilitate the regular monitoring of exterior building walls for ensuring the personal safety of residents living near the building, we propose a method for automatically detecting small defects via images of the building wall surface captured using high-resolution cameras. With this method, the risks caused by loosening or cracking tiles can be easily identified. First, the original detection task is divided between a large-scale segmentation task of non-tile regions and the small-scale detection of defects. Second, corresponding low-resolution deep models are trained and applied to these tasks. Lastly, the results obtained from these multiscale tasks are fused to obtain the comprehensive detection of small defects. Our experimental results indicate that the accuracy and efficiency of the proposed algorithm are superior to those of the single-scale method. The proposed method has achieved excellent results in real-world applications in a residential area, which confirms its high practical value. Copyright ©2021 Journal of Harbin Engineering University.

Keyword:

Walls (structural partitions) Building materials Neural networks

Author Community:

  • [ 1 ] [Sun, Guangmin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Chen, Jiayang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Li, Bing]China National Tobacco Corporation Beijing Corporation, Beijing; 100020, China
  • [ 4 ] [Li, Yu]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Yan, Dong]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address:

  • [li, yu]faculty of information technology, beijing university of technology, beijing; 100124, china

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Source :

Journal of Harbin Engineering University

ISSN: 1006-7043

Year: 2021

Issue: 2

Volume: 42

Page: 286-293

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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