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UAV and Deep Learning for Building Façade Defect Detection: A Comprehensive Review.
Yue Fan1, Yuheng Deng1, Fei Xue1
1State Key Laboratory of Subtropical Building and Urban Science, Center for Human-Oriented Environment and Sustainable Design, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This review systematically evaluates unmanned aerial vehicle (UAV) and deep learning (DL) for building façade defect detection. It highlights the need for integrated pipeline research, from path planning to digital twin integration, for autonomous building management.
Area of Science:
- Building Science
- Artificial Intelligence
- Robotics
Background:
- Unmanned aerial vehicles (UAVs) and deep learning (DL) offer advanced capabilities for intelligent building façade defect detection.
- Current research often isolates technical components, lacking a holistic evaluation of the entire inspection pipeline.
Purpose of the Study:
- To systematically review and analyze the current state of UAV and DL integration for building façade defect detection.
- To identify research gaps and future directions in UAV-based intelligent building inspection.
Main Methods:
- Systematic literature review of 135 peer-reviewed articles (2021-2026) from Web of Science.
- Analysis focused on four key domains: UAV path planning/data acquisition, multi-modal data fusion, DL defect detection algorithms, and 3D reconstruction/digital twin integration.
Main Results:
- UAV path planning needs real-world robustness evaluation.
- Multi-modal data fusion enhances detection but requires balancing lightweight design for edge deployment.
- DL algorithms need joint optimization for efficiency and accuracy on edge devices.
- Digital twin integration for O&M decision-making remains underdeveloped.
Conclusions:
- Advancing UAV-based inspection requires addressing robustness, edge deployment constraints, and semantic integration into digital twins.
- The goal is to transition UAV inspection from an auxiliary tool to a fully autonomous agent for urban built environment management.