Journal Article

UAV and Deep Learning for Automated Detection and Visualization of Façade Defects in Existing Residential Buildings

Zhang, Xiaoxing, Tao, Yiqi, Mai, Jinghua, Xue, Fei, Lau, Stephen Siu Yu, Jiang, San, Fan, Yue, Tsang, Wing Chi

2025

Overview

As urbanization accelerates, façade defects in existing residential buildings have become increasingly prominent, posing serious threats to structural safety and residents’ quality of life. In the high-density built environment of Shenzhen, traditional manual inspection methods exhibit low efficiency and high susceptibility to omission errors. This study proposes an integrated framework for façade defect detection that combines unmanned aerial vehicle (UAV)-based visible-light and thermal infrared imaging with deep learning algorithms and parametric three-dimensional (3D) visualization. Three representative residential communities constructed between 1988 and 2010 in Shenzhen were selected as case studies. The main findings are as follows: (1) the fusion of visible and thermal infrared images enables the synergistic identification of cracks and moisture intrusion defects; (2) shooting distance significantly affects mapping efficiency and accuracy—for low-rise buildings, 5–10 m close-range imaging ensures high mapping precision, whereas for high-rise structures, medium-range imaging at approximately 20–25 m achieves the optimal balance between detection efficiency, accuracy, and dual-defect recognition capability; (3) the developed Grasshopper-integrated mapping tool enables real-time 3D visualization and parametric analysis of defect information. The Knet-based model achieves an mIoU of 87.86% for crack detection and 79.05% for leakage detection. This UAV-based automated inspection framework is particularly suitable for densely populated urban districts and large-scale residential areas, providing an efficient technical solution for city-wide building safety management. This framework provides a solid foundation for the development of automated building maintenance systems and facilitates their integration into future smart city infrastructures.

• Publisher: MDPI AG
• Subject: Accuracy / Automation / building façade defect segmentation / Building information modeling / coordinate transformation / Coordinate transformations / Deep learning / Defects / Efficiency / Energy consumption / Genetic algorithms / Heat detection / High rise buildings / high-density cities / Localization / mapping / Neighborhoods / Residential buildings / Semantics / Thermography / unmanned aerial vehicle (UAV) / Unmanned aerial vehicles / Urban renewal