Search Results (673)

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process. Full article

Objective: This study investigates the influence of selecting three thermoplastics as raw materials (PLA, PETG, and ABS) on dimensional accuracy, defect formation, and slicer-based mass prediction reliability in FFF 3D-printed UAV micro-frames. Methods: A factorial experimental design combining three materials, two micro-frame geometries, and two infill levels was implemented. Print quality was assessed through structured visual inspection of common FFF defects, while manufacturing reliability was evaluated by comparing slicer-predicted and experimentally measured mass. Dimensional fidelity was quantified at critical motor mount features using repeated micrometric measurements and dedicated accuracy and uniformity indices. Results: The results reveal strong material-dependent behaviour. PLA exhibited the highest dimensional consistency and near-zero mean mass prediction error, PETG showed intermediate performance, and ABS presented significant warping, together with a pronounced positive mass prediction bias. These findings indicate systematic discrepancies between predicted and measured mass values and highlight the need for material-dependent calibration of slicing software. Conclusions: Material selection and process calibration strongly affect dimensional fidelity and manufacturing reliability in FFF-printed UAV micro-frames. The findings provide practical guidance for material choice and slicing parameter adjustment in UAV fabrication and similar small-scale FFF applications. Full article

The growing demand for energy-efficient buildings and the urgent need to retrofit aging infrastructure have driven increased interest in advanced diagnostic technologies. Among these, unmanned aerial vehicle (UAV)-based infrared thermography (IRT) has emerged as a promising non-destructive technique for assessing the thermal performance of building envelopes. This review examines recent developments and applications of dynamic infrared thermography (IRT) in the building sector for both qualitative and quantitative thermal assessment, based on previously conducted studies. It highlights the increasing adoption of integrated UAV-based IRT for building inspection and diagnostics, and critically reviews the operational, technical, and methodological advancements in dynamic thermography achieved over the past decade. Furthermore, the review presents a comprehensive framework for operational planning, encompassing environmental conditions, infrared camera configuration, and optimal UAV flight parameters. The key findings identify major challenges associated with dynamic IRT applications, particularly those related to measurement accuracy that currently limit its use for quantitative assessments and synthesize proposed methodologies to address these limitations. The review also highlights the absence of standardized procedures for determining emissivity and reflected apparent temperature in dynamic measurement setups and discusses potential approaches to overcome these gaps. Finally, it outlines priority directions for future research to support the reliable and consistent application of dynamic IRT in quantitative analysis and provides a reference for energy auditors and thermography practitioners to inform the selection of appropriate procedures for accurately quantifying heat loss in building envelopes. Full article

Ensuring the structural integrity and operational safety of oil and gas pipelines is a critical challenge due to their extensive geographical coverage and exposure to environmental and anthropogenic risks. Traditional inspection approaches including ground patrols and manned aerial surveys are labor-intensive, costly, and often lack real-time responsiveness. While unmanned aerial vehicles (UAVs) enable flexible and high-resolution monitoring, their practical deployment requires lightweight, robust detection models capable of real-time inference on embedded edge hardware under heterogeneous environmental conditions. This paper presents an end-to-end, edge-deployable UAV inspection framework for simultaneous detection of above-ground pipelines and visually observable anomaly/leak indicators using the official Ultralytics YOLOv26n object detector. A curated dataset of 6127 UAV images acquired across desert, semi-urban, and industrial environments was annotated with two classes (Pipeline and Anomaly/Leak) and partitioned into training 87.5%, validation 8.3%, and testing 4.2% subsets. The detector was fine-tuned from COCO-pretrained weights for 300 epochs at 600 × 600 resolution and evaluated using COCO-style metrics. On the held-out test set, the proposed model achieved 92.4% mAP@0.5 and 75.0% mAP@0.5:0.95, with 89.7% precision, 90.2% recall, and 89.9% F1-score at the selected operating threshold. Optimized TensorRT deployment on an NVIDIA Jetson Xavier NX sustained real-time inference at 18 FPS, demonstrating suitability for onboard UAV processing. Rather than proposing a new detector architecture, the study contributes a domain-specific annotated UAV dataset, deployment-oriented benchmarking, and an end-to-end edge inference workflow for corridor-scale monitoring. The proposed framework can help reduce environmental contamination risk and improve personnel safety during pipeline inspection. Full article

Road maintenance remains one of the most resource-intensive and hazardous operations in infrastructure management. Traditional inspection practices rely heavily on manual labour and discrete procedures, often resulting in limited scalability, operator exposure to traffic hazards, and inefficiencies in data collection. This paper presents a novel automated methodology that integrates Unmanned Aerial Vehicles (UAVs) and autonomous mobile robots (AMRs) to enable automated inspection and measurement of road assets through a digital twin (DT) system. The system leverages data fusion and real-time synchronisation between field agents and a centralised digital twin to monitor the retro-reflectivity of vertical and horizontal signage, detect obstacles and vegetation, and support data-driven maintenance planning. A case study conducted on the Italian highway network demonstrated improvements in operational safety, inspection efficiency, and measurement consistency. The results confirm that the integration of UAVs and AMRs within a digital twin framework can significantly improve sustainability, productivity, and workers’ safety in road maintenance operations. Full article

To support the long-term monitoring and preventive conservation of linear cultural heritage, this study proposes a UAV-LiDAR DEM-constrained SBAS-InSAR long-term time-series monitoring method to identify the spatiotemporal deformation patterns and risk-sensitive segments of the near-field ground surface along the Huairou Great Wall. Unlike traditional methods, this research is the first to apply high-resolution UAV-derived DEM for topographic correction and phase modeling in the Huairou Great Wall, aiding in long-term ground deformation monitoring. By integrating multi-scale meteorological data such as precipitation, temperature, and humidity, the study systematically analyzes their impact on deformation. The results reveal significant heterogeneity in ground deformation along the Huairou Great Wall, with the Jiankou section identified as a sensitive area. The study shows a clear event-scale correspondence between rainfall and short-term deformation fluctuations, while air temperature and relative humidity exhibit statistical consistency with cumulative deformation, serving as perturbation cues for sensitivity screening but not direct causal attribution. Compared to traditional ground-based monitoring methods, this approach significantly reduces labor and time costs, enabling large-scale, high-precision, long-term monitoring in a shorter period. It provides a technical basis for identifying risk-prone segments along the Great Wall and conducting post-rainfall inspections, providing a reference for the long-term monitoring and preventive protection of linear cultural heritage. Full article

Small rotorcraft unmanned aerial vehicles (UAVs) are highly susceptible to wind disturbances when performing tasks such as fixed-point hovering, low-altitude inspection, and aggressive maneuvers. Under complex, variable meteorological conditions, attitude stability and position-holding accuracy are particularly critical. Although quadrotor UAVs exhibit structural and dynamic symmetry, real wind disturbances are often asymmetric, disrupting the original balance and leading to intensified attitude oscillations, position drift, and degraded data quality. To effectively address the challenges of wind-induced oscillation and positional deviation, this paper proposes a fuzzy logic-based linear active disturbance rejection control (Fuzzy-LADRC) strategy. This approach employs a hybrid algorithm combining particle swarm optimization and gray wolf optimization to optimize controller parameters and incorporates fuzzy logic to enhance the adaptive capability of the linear active disturbance rejection controller (LADRC). Simulation experiments conducted in MATLAB/Simulink under complex wind-field conditions demonstrate that the proposed method significantly outperforms traditional PID controllers: in the regulation of roll and pitch angles, control performance improves by approximately 5%, while in yaw angle control, the improvement reaches up to 30%. Furthermore, this method can significantly suppress position deviation and fluctuation in the X and Y directions, and reduce the overshoot in the Z-axis during the UAV’s takeoff phase by 75%. Full article

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this paper proposes an improved RTDETR-based detection framework, termed CEA-DETR, for wind turbine blade surface defect inspection. First, a Cross-Scale Multi-Edge feature Extraction (CSME) backbone is designed by integrating multi-scale pooling and edge-enhancement units with a dual-domain feature selection mechanism, enabling effective extraction of fine-grained texture and edge features across different scales. Second, an Efficient Multi-Scale Feature Fusion Network (EMSFFN) is constructed to facilitate deep cross-level feature interaction through adaptive weighted fusion and multi-scale convolutional structures, thereby enhancing the representation of multi-scale defects. Furthermore, an adaptive sparse self-attention mechanism is introduced to reconstruct the AIFI module, strengthening global dependency modeling and guiding the network to focus on critical defect regions under complex background conditions. Experimental results demonstrate that CEA-DETR achieves mAP50 and mAP50:95 of 89.4% and 68.9%, respectively, representing improvements of 3.1% and 6.5% over the RT-DETR-r18 baseline. Meanwhile, the proposed model reduces computational cost (GFLOPs) by 20.1% and parameter count by 8.1%. These advantages make CEA-DETR more suitable for deployment on resource-constrained unmanned aerial vehicles (UAVs), enabling efficient and real-time autonomous inspection of wind turbine blades. Full article

Artificial Intelligence (AI)-based crack detection in buildings uses computer vision and deep learning to automatically identify structural cracks from inspection images. In recent years, many studies have explored this topic, but the overall development of the field, its methodological practices, and the remaining challenges are still not fully clear. Unlike most previous reviews that focus mainly on technical methods, this study combines a large-scale scientometric mapping of the research field with a focused technical analysis of recent AI-based crack detection methods specifically applied to building structures. This study therefore provides a dual-layer review covering research published between 2015 and 2025. A total of 146 Scopus-indexed publications were analysed using Visualization of Similarities viewer (VOSviewer) to examine publication growth, thematic evolution, collaboration patterns, and citation structures. In addition, a focused technical review of 36 highly relevant studies was carried out to analyse task formulations, model families, datasets, evaluation protocols, and methodological practices. The results show a rapid increase in research activity after 2020, largely driven by advances in deep-learning and Unmanned Aerial Vehicle (UAV)-based inspections. At the same time, collaboration networks remain uneven, and citation influence is concentrated in a limited number of research communities. The technical review further shows that most studies focus on detection-level tasks, particularly You Only Look Once (YOLO)-based models, while predictive diagnostics, automated inspection reporting, and decision-oriented Structural Health Monitoring (SHM) are still rarely addressed. Current datasets and evaluation protocols also remain mostly perception-oriented, which makes it difficult to assess robustness, generalisability and long-term predictive capability. Full article

The growing scale and complexity of industrial infrastructure make efficient and reliable inspections a critical challenge. Inspection task demands often vary dynamically, requiring efficient and demand-responsive inspection strategies to ensure stable operation. However, existing UAV inspection approaches typically deploy UAV base stations (UAV-BSs) based on fixed inspection frequencies, which are inadequate for adapting to such dynamic demands and may reduce inspection efficiency. Moreover, these approaches often rely on historical inspection data, whose empirical distributions may deviate from the true distributions, thereby compromising solution robustness. To address these issues, this paper proposes a two-stage distributionally robust optimization (TDRO) framework for joint UAV-BS deployment and inspection routing in dynamic environments. The framework accounts for uncertainties in both inspection frequency and distributional perturbations. Uncertainty sets constructed based on probability metrics are employed to capture deviations between empirical and true distributions, forming the foundation of the two-stage distributionally robust optimization model. The resulting model is solved using column-and-constraint generation (C&CG) integrated with column generation (CG), yielding robust deployment decisions and an effective trade-off between total system cost and inspection efficiency. Simulation results show that the framework effectively addresses inspection frequency uncertainty, reducing the total objective by 5.50% on average, with a further 2.16% reduction when distributional perturbations are considered. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article

The inspection of minuscule insulator defects from high-resolution (HR) UAV imagery presents a significant algorithmic challenge. The severe scale mismatch between HR images and low-resolution model inputs often leads to feature distortion for sparsely distributed targets. To address these issues, this paper proposes an integrated data–model collaborative framework. At the data level, an offline label-guided optimal tiling (LGOT) strategy is introduced to alleviate scale mismatch by curating information-dense training tiles. At the model level, we design the semi-decoupled prior-driven detection head (SDPD-Head), which leverages evolutionary priors to stabilize the learning of microscopic spatial features. During inference, an online inference-time adaptive tiling (ITAT) strategy is used to match the spatial scale distribution between training and inference and to reduce feature loss caused by direct downscaling. Experiments on a real-world inspection dataset show that the proposed framework achieves an mAP@50 of 92.9% with 2.17 M parameters and 4.7 GFLOPs. Full article

Unmanned aerial vehicles (UAVs) are increasingly being used as diagnostic platforms in electricity transmission and distribution, enabling safer and faster inspections compared to manual climbing operations or manned aerial support. This article presents an implementation-oriented inspection process that integrates RGB imaging, infrared (IR) thermography and (optionally) LiDAR documentation for critical energy infrastructure and photovoltaic (PV) installations. The survey consists of two stages: a preliminary stage under controlled conditions and an operational stage in a real-world environment, limited only by UAV flight restrictions. Thermal measurements are recorded in radiometric formats and analyzed using polygon- and profile-based tools to identify temperature anomalies (hot spots) and support maintenance escalation decisions. This manuscript presents standardized sample templates for mission logs, QA/QC activities, and anomaly lists, intended to support reproducible data collection in future studies. The proposed process supports predictive maintenance by enabling repeatable inspections, archive-based trend analysis, and integration with asset management processes, while minimizing operational risk and avoiding power outages when technically feasible. Full article

Underground mine inspection is a critical operation for safety and resource management. It presents unique challenges, including confined spaces, harsh environments, and the lack of reliable positioning systems. This paper presents the design, development, and evaluation of an Unmanned Aerial Vehicle (UAV) specifically engineered for supervised autonomous inspection in subterranean scenarios. Key technical contributions include mechanical adaptations for collision tolerance, an optimized sensor-actuator selection for navigation, and the deployment of a mission-governing state machine for seamless autonomous acquisition. Furthermore, we detail the data treatment workflow, employing a multi-modal point cloud registration technique that successfully integrates high-resolution visual-depth scans of critical mine pillars into a comprehensive, globally referenced map derived from Light Detection and Ranging (LiDAR) data of the entire workspace. We show experiments that illustrate and validate our approach in two real-world scenarios, a simulated coal mine used to train mine rescue teams and an operating Limestone mine. Full article

Insulators are essential components in high-voltage transmission lines and require regular inspection to ensure reliable power delivery. Traditional manual inspection methods are inefficient and labor intensive, highlighting the need for intelligent and automated solutions. In this study, we propose a missing insulator detection method that integrates Unmanned Aerial Vehicle (UAV) imaging with deep learning techniques. Firstly, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) is employed to detect and localize insulators in aerial images. Secondly, the localized insulators are segmented using an improved U-Net to reduce background interference. A bounding box regression approach is adopted to obtain the minimum enclosing rectangles, and the insulators are aligned vertically. Adaptive thresholding is then applied to extract binary images of the insulators. These binary images are further transformed into defect curves, from which missing insulators are identified based on curve distribution. To address the limited availability of labeled samples, a transfer learning-based strategy is adopted to improve model generalization. A dataset of glass insulators was collected using a DJI M300 UAV equipped with an H20T camera along a 330 kV overhead transmission line. On the collected UAV insulator dataset, the proposed method achieved an AP@0.5 of 99.85% and an average IoU of 88.56% for insulator string detection, while the improved U-Net achieved an mIoU of 89.73% for insulator string segmentation. Outdoor flight experiments further verified performance under varying backgrounds and illumination conditions in our UAV inspection scenarios. Full article