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Remote Sensing
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Unmanned Aerial Vehicle-Based Inspection in Infrastructure Maintenance
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Unmanned Aerial Vehicle-Based Inspection in Infrastructure Maintenance

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 8042

Editors

Dr. Jiayong Yu

E-Mail Website
Guest Editor
Key Laboratory for Wind and Bridge Engineering of Hunan Province, Hunan University, Changsha 410082, China
Interests: road inspection; bridge displacement; civil engineer; UAV; drones GNSS; Beidou; remote sensing
Prof. Dr. Wujiao Dai

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, South Lushan Road, Changsha 410083, China
Interests: UAV inspection; machine vision; deep learning; deformation monitoring; precision navigation and positioning
Prof. Dr. Jiangpeng Shu

E-Mail Website
Guest Editor
College Civil Engineering & Architecture, Zhejiang University, Hangzhou 310058, China
Interests: deep learning; image analysis; robotics; structural inspection and evaluation; digital twin; building information modeling (BIM); automated construction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qian Fan

E-Mail Website
Guest Editor
College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
Interests: structural health monitoring; road damage inspection; beidou/GNSS positioning; deformation monitoring; UAV remote sensing

Special Issue Information

Dear Colleagues,

The rapid advancements in unmanned aerial vehicle (UAV) technology have revolutionized the field of infrastructure inspection. UAVs are now integral tools for performing detailed and efficient inspections of critical infrastructure, offering significant improvements over traditional methods. Their ability to access difficult-to-reach areas, coupled with advancements in sensor technologies (e.g., high-resolution imaging, LiDAR, thermal sensing), enables real-time, high-precision monitoring. This Special Issue explores the growing role of UAVs in infrastructure maintenance, focusing on their application in the monitoring, inspection, and assessment of structures such as bridges, roads, dams, and power lines.

This Special Issue aims to provide a comprehensive overview of the latest developments in UAV-based infrastructure inspection techniques. It seeks to highlight the potential of UAVs to enhance the accuracy, efficiency, and safety of infrastructure maintenance practices. This Special Issue aligns with the journal's scope, emphasizing the intersection of remote sensing technologies and infrastructure management. We invite contributions that address both the technological advancements and practical applications of UAVs in infrastructure maintenance. Articles may address, but are not limited to, the following topics:

  • UAV-based surface damage identification in bridges, roads, slopes, and dams;
  • UAVs for detecting corrosion and cracking in steel and concrete structures;
  • UAV-based infrastructure deformation monitoring;
  • Deep learning for detection of surface damage;
  • Machine vision-based methods for detection of surface damage in infrastructure;
  • Sensor technologies covering machine vision, multispectral, thermal, LiDAR, etc.;
  • Multi-source heterogeneous data fusion and damage detection;
  • Non-contact monitoring for infrastructure.

Dr. Jiayong Yu
Prof. Dr. Wujiao Dai
Prof. Dr. Jiangpeng Shu
Prof. Dr. Qian Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • unmanned aerial vehicle (UAV)
  • infrastructure maintenance
  • remote sensing
  • inspection
  • LiDAR
  • thermal imaging
  • machine vision
  • structural health monitoring
  • multi-sensor technologies
  • deep learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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32 pages, 39769 KB  
Article
DFELD-YOLO: A Decoupled Lightweight Detection Model for UAV-Based Wind Turbine Blade Damage Inspection
by Xuwen Zhang, Huilin Tang, Boyan Hu, Hongmei Li and Xin Shu
Remote Sens. 2026, 18(14), 2422; https://doi.org/10.3390/rs18142422 - 21 Jul 2026
Viewed by 575
Abstract
Accurate and efficient detection of surface damage on wind turbine blades is important for ensuring the safe operation and maintenance of wind farms. Existing models have problems of irreversible loss of micro-damage features, insufficient modeling of long cracks, and weak anti-interference ability. To [...] Read more.
Accurate and efficient detection of surface damage on wind turbine blades is important for ensuring the safe operation and maintenance of wind farms. Existing models have problems of irreversible loss of micro-damage features, insufficient modeling of long cracks, and weak anti-interference ability. To address these issues, we propose a Decoupled Feature Enhancement and Pixel-preserving Downsampling YOLO model (DFELD-YOLO). The model features the following three innovations: (1) Feature extraction and downsampling are innovatively decoupled to construct DFELDown, which completes feature extraction via an attention mechanism and achieves pixel-preserving downsampling through a Cw-SPD transformation, effectively solving the problem of micro-damage feature loss. (2) We built a lightweight anti-interference GhostSEC3 module, which reduces parameters and computations while adaptively suppressing background interference. (3) We designed a Cross-Shaped Stripe Attention Module (C2CSModule), which achieves a global receptive field with linear complexity, while accurately capturing continuous features of long cracks. Extensive experiments on the UAV-based wind turbine blade damage dataset show that DFELD-YOLO has 2.05 M parameters and 5.9 GFLOPs, with 20.8% and 7.8% reductions compared with the baseline YOLOv11, respectively. The lightweight properties make it suitable for deployment on edge devices, including UAVs. Meanwhile, it achieves a 3.4% improvement in mAP@0.5. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle-Based Inspection in Infrastructure Maintenance)
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22 pages, 6482 KB  
Article
Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks
by Feng Han and Chongshi Gu
Remote Sens. 2025, 17(15), 2668; https://doi.org/10.3390/rs17152668 - 1 Aug 2025
Cited by 6 | Viewed by 2072
Abstract
Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial [...] Read more.
Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle-Based Inspection in Infrastructure Maintenance)
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28 pages, 13711 KB  
Article
BIM-Based Trajectory Planning for Unmanned Aerial Vehicle-Enabled Box Girder Bridge Inspection
by Jiangpeng Shu, Zhe Xia and Yifan Gao
Remote Sens. 2025, 17(4), 682; https://doi.org/10.3390/rs17040682 - 17 Feb 2025
Cited by 9 | Viewed by 2612
Abstract
Inspection is essential for bridge maintenance and has been supplemented by the use of unmanned aerial vehicle (UAV) photogrammetry. However, a review of the literature reveals that existing approaches require the intervention of a human operator to select waypoints in digital twin environments. [...] Read more.
Inspection is essential for bridge maintenance and has been supplemented by the use of unmanned aerial vehicle (UAV) photogrammetry. However, a review of the literature reveals that existing approaches require the intervention of a human operator to select waypoints in digital twin environments. Thus, existing studies are limited by either manual or semi-automatic control restrictions on UAV navigation settings, which is the main motivation for our work. This research developed a building information modelling (BIM)-based trajectory planning approach to enable fully autonomous UAV navigation for box girder bridge inspection, where the UAV follows a predetermined sequence to traverse the environmental space of a box girder bridge. The approach reflects the geometry properties of box girders as inspection characteristics and is designed with algorithms to determine a mathematical relationship between the box girders’ coordinates and inspection sequences. Field testing of the approach shows that it has satisfactory performance in terms of (1) navigation efficiency, (2) reasonableness of the sequence determined for trajectory feature points, (3) trajectory closeness and deviation, and (4) trajectory smoothness. Its satisfactory performance supports the practicability of fully autonomous UAV-enabled bridge inspection. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle-Based Inspection in Infrastructure Maintenance)
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Review

Jump to: Research

41 pages, 4419 KB  
Review
A Review of UAV-Based Crack Detection in Civil Infrastructure: A Multi-Level Visual Analysis Framework, Scene Adaptability, and Challenges
by Yue Bai, Wei Quan, Xuming Shi, Zeyi Yan and Guoliang Yuan
Remote Sens. 2026, 18(11), 1806; https://doi.org/10.3390/rs18111806 - 2 Jun 2026
Cited by 3 | Viewed by 1194
Abstract
Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) [...] Read more.
Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) remote sensing has become an important approach for Structural Health Monitoring (SHM), owing to its high spatial resolution imaging capability and superior operational flexibility. Nevertheless, existing studies focus on optimizing individual algorithms, lacking a systematic analysis oriented toward multi-scenario engineering applications. Therefore, we present a comprehensive review of UAV-based crack detection techniques for infrastructure using remote sensing imagery. First, publicly available datasets, UAV platforms, and evaluation metrics are systematically summarized. Then a multi-level visual analysis framework for UAV inspection is established. The framework categorizes existing methodologies into five levels: image-level classification, object-level detection, pixel-level segmentation, geometric quantification, and three-dimensional (3D) reconstruction, followed by a systematic evaluation of representative methods. Furthermore, the applicability of different methods across diverse scenarios, including bridges, pavements, dams, building facades and wind turbine blades, is systematically explored. Finally, the key challenges and future research directions are discussed. This review aims to provide a systematic theoretical foundation and methodological reference for advancing UAV-based infrastructure crack inspection from algorithm development toward practical multi-scenario engineering applications. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicle-Based Inspection in Infrastructure Maintenance)
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