Visual Detection Technology and Its Application in Urban Building Safety Monitoring

A Special Issue of Buildings (ISSN 2075-5309) belonging to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 779

Editor


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Guest Editor
College of Construction Engineering, Jilin University, Changchun 130026, China
Interests: UAV; intelligent sensing; 3D reconstruction

Special Issue Information

Dear Colleagues,

With the rapid advancement of urbanization, urban buildings are facing potential safety risks such as cracks, tilting, deformation, and aging during long-term service. Traditional manual inspection methods are inefficient, high-risk, and prone to omissions, especially for high-rise buildings and complex urban environments, where it is difficult to achieve comprehensive and accurate safety monitoring. Regarding the safety of urban residents' lives and property, as well as the stable operation of urban functions, it is necessary to develop efficient, accurate, and intelligent inspection technologies. However, it is not enough to simply obtain visual images of buildings; the identification, evaluation, and early warning of potential safety hazards (such as cracks and building tilting) based on visual detection data should be further realized.

This Special Issue aims to gather innovative research and development achievements in the field of visual detection for urban construction, focusing on safety monitoring of urban buildings, so as to promote the application of visual detection technology in urban safety management and improve the level of urban disaster prevention, mitigation, and safety guarantee. The scope of this Special Issue covers original research and review studies, including (but not limited to) the following:

  • Visual detection technology for urban buildings (with UAV as a typical application platform);
  • Visual identification and measurement of building cracks based on visual detection technology (including UAV-borne visual detection);
  • Urban building tilting monitoring and deformation analysis based on visual detection technology;
  • Visual data processing and intelligent interpretation for urban building monitoring;
  • AI-based visual image analysis for urban safety hazard early warning (including UAV remote sensing detection images);
  • Visual detection system design and optimization for urban construction (including UAV-based visual inspection systems);
  • Application of UAV LiDAR, multi-spectral imaging, and other visual remote sensing detection technologies in urban building safety monitoring;
  • Safety evaluation of urban buildings based on visual detection data (including UAV visual remote sensing data).

Dr. Tengyue Li
Guest Editor

Manuscript Submission Information

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Keywords

  • visual detection technology
  • urban building
  • safety monitoring
  • visual data
  • building cracks
  • UAV image processing
  • deformation analysis
  • AI-based image analysis

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Published Papers (1 paper)

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Research

26 pages, 23083 KB  
Article
Inspection System for Bridge Surface Defects in Cold Regions Based on Parameter Sharing and Feature Enhancement
by Qipeng Yang, Yuchen Xie, Danfeng Du and Linji Cheng
Buildings 2026, 16(16), 3248; https://doi.org/10.3390/buildings16163248 - 16 Aug 2026
Viewed by 329
Abstract
To address the scarcity of bridge defect data in the harsh environments of cold regions, as well as the parameter redundancy and edge platform deployment challenges of existing algorithms, this paper proposes an intelligent inspection system for bridge surface defects in cold regions [...] Read more.
To address the scarcity of bridge defect data in the harsh environments of cold regions, as well as the parameter redundancy and edge platform deployment challenges of existing algorithms, this paper proposes an intelligent inspection system for bridge surface defects in cold regions based on parameter sharing and feature enhancement. The system first constructs a large-scale dataset called CRBD (Cold-Region Bridge Defect), which contains 10,129 high-resolution images and finely classifies defects into four standardized categories: Crack, Spalling, Patch, and Seepage. Subsequently, a lightweight detection network called BridgeNet is designed. Its core parameter sharing and feature enhancement detection head stabilizes training via group normalization, significantly reduces the parameter count through cross-scale global sharing and structural reparameterization, and improves bounding-box regression accuracy by incorporating a distribution focal loss mechanism. On this basis, an airborne real-time image processing and intelligent perception pipeline is constructed, which establishes the complete workflow for autonomous unmanned aerial vehicle inspections. The experimental results demonstrate that with a lightweight architecture of only 2.26 M parameters and a model size of 4.98 M, BridgeNet achieves a mean Average Precision of 61.4% and an F1 Score of 60.9%. Furthermore, it exhibits excellent real-time inference speed on heterogeneous edge mobile platforms and maintains robust overall perception stability under various extreme physical disturbances. Full article
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