Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

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

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Oxidative Functionalization of Woody Biochar for Hexavalent Chromium Detoxification: Adsorption-Coupled Reduction and Dual-Phase Remediation.

Molecules (Basel, Switzerland)·2026

Same author

SGLT2 inhibitors and GLP-1 receptor agonists in atrial fibrillation treatment: Clinical evidence and mechanistic insights.

Metabolism: clinical and experimental·2026

Same author

The role of embodied musical engagement in enhancing non-cognitive skills and rehabilitation outcomes in children with special needs.

Frontiers in psychology·2026

Same author

Effectiveness of sports injury prevention programs for adolescent and children football players: a meta-analysis and systematic review.

PeerJ·2026

Same author

Versatile Nano-Crosslinker Enhanced Injectable Hydrogel Toward Rapid Hemostasis and Efficient Trauma Repair.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Structural aging of a cohesive and amorphous granular solid under cyclic loading.

Soft matter·2026

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.

Keywords:

deep learning defect detection defect identification digital twin multi-modal data fusion path planning unmanned aerial vehicle (UAV)

Related Experiment Videos

Last Updated: Jul 7, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

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.