Search Results (9,401)

Improving pesticide application efficiency in dense pepper canopies requires optimization of Unmanned Aerial Vehicle (UAV) operational parameters based on both droplet deposition and pest control performance. This study evaluated the effects of flight height and nominal droplet size setting on canopy deposition characteristics and pest control efficacy using a DJI T60 plant protection UAV under field conditions. Three flight heights (2, 3, and 4 m) and five nominal droplet size settings (100, 150, 200, 250, and 300 µm) were investigated. Droplet coverage, deposition density, canopy penetration rate, deposition uniformity, and corrected control efficacy were used as evaluation indicators, and TOPSIS was applied for comprehensive parameter optimization. The results showed that both flight height and nominal droplet size setting exert a certain influence on droplet deposition characteristics and pest control efficacy. Under the specific conditions tested in this study, the 3 m flight height achieved favorable overall deposition performance, while the 250 µm nominal setting exhibited the highest effective coverage among the tested droplet size treatments. Pest control efficacy showed a consistent trend with droplet deposition performance, and the 3 m + 250 µm nominal setting combination achieved the highest corrected control efficacy at 7 DAT. According to the TOPSIS evaluation, the combination of 3 m flight height and 250 µm nominal droplet size setting achieved the best overall performance under the experimental conditions. Because actual airborne droplet size spectra were not independently measured, the recommendation refers to the tested operational setting rather than a measured droplet size population. Full article

Reliable localization without GPS is a prerequisite for autonomous unmanned aerial vehicles (UAVs) operating inside warehouses, where a lightweight monocular camera paired with an inertial measurement unit (IMU) and onboard computer are the minimal sensing and processing an onboard platform can carry. This paper presents a tightly coupled monocular point-feature visual-inertial odometry (VIO) system for that setting, realized on a GTSAM fixed-lag factor graph with inverse-depth landmarks, on-manifold IMU preintegration, and an online loop-closure pose graph. The system is developed as the initial estimation stage of an autonomous stock-management UAV under development for indoor logistics warehouses. The decisive design element is the bootstrap: the metric, gravity-aligned initialization of a monocular estimator is well conditioned only under a translation-rich trajectory, a condition the near-zero-baseline pickup and takeoff transient that opens every indoor flight violates. Building on the visual-inertial alignment of VINS-Mono, we harden this step with a pre-bundle-adjust conditioning gate and a continuous-window initialization that refines the whole bootstrap window inside the smoother instead of freezing a single seed. On all eleven EuRoC MAV sequences, indoor flight tests recorded onboard a micro air vehicle in an industrial hall and two instrumented rooms, one fixed configuration per operating environment converges on every sequence, including three that otherwise diverge by tens to thousands of meters, and, driven by the same feature stream as locally run VINS-Mono and PL-VINS baselines, attains the better pure-odometry accuracy on nine of the eleven, with ATE RMSE of 0.12–0.37 m on the Machine Hall, a margin a paired signed-rank test confirms against VINS-Mono and leaves unconfirmed against PL-VINS at this sample size. We identify the stock fixed-lag marginalization as the principal consistency limitation and outline First-Estimates-Jacobian marginalization as the route to a more consistent estimator, establishing a characterized point-only baseline on one public benchmark as the starting point for subsequent on-platform work. Full article

Facade debonding may lead to falling components and pose safety risks in dense urban environments, but infrared thermal responses may also arise from non-defect facade components and environmental conditions. Conventional facade inspection methods are often labor-intensive, hazardous, and difficult to integrate into digital maintenance workflows. To support safer and more efficient facade inspection and maintenance information management, this study develops an engineering-oriented inspection and management framework that integrates unmanned aerial vehicle infrared thermography, intelligent defect recognition, visual result verification, and defect information management. A UAV-based infrared data acquisition scheme was established, and a self-constructed dataset containing 1035 thermal images was developed for the detection of suspected facade debonding regions and common thermal interference sources, including windows, air-conditioning units, and signage. A lightweight detection model was embedded as the recognition engine of the framework to balance detection reliability and deployment efficiency under practical inspection conditions. Experimental results show that the proposed method achieved an mAP@0.5 of 87.8%, with 1.64 million parameters and 4.2 GFLOPs, indicating its potential for rapid preliminary facade screening under the tested computing configuration. Beyond model evaluation, an application platform was developed to support infrared image and video input, automatic detection, result visualization, statistical analysis, and defect record storage. The proposed framework demonstrates the potential of combining UAV infrared inspection and digital management tools for preliminary facade screening and inspection documentation, providing supporting information for subsequent engineering review and maintenance planning. Full article

Unmanned Aerial Vehicle-borne Ground-Penetrating Radar (UAV-GPR) has been used for ice thickness monitoring in lakes and rivers due to its non-contact measurement, high resolution, and operational flexibility. Existing algorithms can extract ice layer boundaries by tracking continuous bottom reflections in GPR images. However, they fail when the radar signal lacks a clear bottom reflection—a common condition in ice layers containing unfrozen water—and manual interpretation remains time-consuming. To address this limitation, this paper builds a freshwater ice GPR dataset covering both fully frozen and unfrozen water-bearing zones, and proposes a method for ice thickness identification based on the DeepLabv3+ neural network. The model performs pixel-level binary classification, labeling each pixel as ice layer or background, and generates a segmentation mask that constrains the subsequent thickness calculation to valid ice regions only. Field validation against drilling measurements demonstrates that the model achieves Intersection over Union (IoU) of 97.12% and an F1-score of 98.54% for ice layer identification, with a relative error in ice thickness measurement below 3% based on five borehole measurements. Field tests in two reservoirs across Tibet and Jilin, China, demonstrate that the proposed method can accurately characterize the distribution and thickness of the ice layer while effectively eliminating the interference of unfrozen water zones. The results demonstrate that the proposed method can provide automated, accurate ice thickness estimates for UAV-GPR surveys of freshwater ice. Full article

Unvegetated tidal flats store sedimentary organic carbon (OC), but their contribution to blue-carbon inventories remains poorly constrained because sparse cores must represent heterogeneous intertidal environments. This study quantified OC stock within the upper 1 m of Hwangdo tidal flat, South Korea, by integrating ten 100-cm cores, 107 surface-sediment samples, and an unmanned-aerial-vehicle-derived digital elevation model covering 4.841 km². The surface survey characterized variations in elevation, sediment texture, and OC, while the cores provided profiles of grain size, dry bulk density, and OC content. Core stocks were integrated over 0–100 cm and evaluated using a spatially grouped, cross-validated adjustment based on surface elevation and uppermost-sediment OC to reduce bias from sparse core distribution. Measured core stocks ranged from 13.61 to 30.20 Mg C ha⁻¹, with an unadjusted mean of 23.04 Mg C ha⁻¹. Mean OC contents were low in surface sediments (0.18%) and core sediments (0.17%). Across the pooled surface and core dataset, TOC correlated positively with mud content (Spearman’s ρ = 0.47) and negatively with sand content (ρ = −0.51), indicating sediment-textural control over OC distribution. The adjustment increased the mean stock by 5.9% to 24.39 Mg C ha⁻¹. Applying this value to 484.108 ha yielded a regional OC storage of 11.81 Gg C, with a conditional bootstrap interval of 9.12–12.90 Gg C. However, the low cross-validated Q² (0.070) shows that the model adjusts the core mean rather than providing a spatially explicit prediction. Overall, Hwangdo’s comparatively low OC pool likely reflects the combined effects of limited inputs of terrestrial organic matter; the absence of vascular vegetation and the associated belowground biomass that would otherwise promote carbon retention; and the frequent reworking, oxygenation, and export of organic matter from its sand-dominated, hydrodynamically energetic sediments. By integrating depth-resolved cores with dense surface observations and UAV-derived topography while explicitly reporting conditional uncertainty, this study establishes a transferable framework for more defensible blue-carbon inventories and strategically targeted sampling in data-limited unvegetated tidal flats. Full article

Unmanned aerial vehicle (UAV) swarms increasingly operate in GNSS-denied environments, where cooperative localization provides relative positioning by fusing onboard odometry with inter-agent range measurements, for which ultra-wideband (UWB) two-way ranging is a common infrastructure-free choice. Beyond accuracy, safe swarm autonomy needs a trustworthy measure of positioning uncertainty, since collision-avoidance and formation-keeping decisions derive their safety margins from the reported covariance. Under sustained agile flight, both are hard to achieve at once: motion within each time-division multiple access (TDMA) polling round induces a ranging bias well above the UWB noise floor, and reusing shared information across the network drives the reported covariance below the true error. To address these two problems jointly rather than in isolation, we propose a modular architecture coupling an online maximum-likelihood polynomial least-squares (MPLS) ranging front-end with a fading split covariance intersection (SCI) cooperative back-end through a per-link variance interface: online MPLS compensates the motion-induced bias and reports a calibrated, time-varying variance that fading SCI consumes as measurement noise while its continuous-time fading factor bounds the reused-information covariance. Monte Carlo simulation over anchored and anchor-free 16-node swarms shows the two effects to be empirically decoupled, with front-end ranging quality governing positioning accuracy and back-end correlation handling governing estimator consistency. The method attains sub-meter positioning accuracy in both settings and, without per-scenario tuning, keeps consistency—quantified by the average normalized estimation error squared (ANEES)—within a trusted band; comparably accurate extended Kalman filter and covariance-intersection baselines fall outside it, becoming overconfident and overconservative, respectively. It thus delivers the trustworthy uncertainty that safety-critical swarm decisions require in GNSS-denied flight. Full article

Climate-related and environmental hazards affect cultural heritage sites in markedly different inland, coastal, lacustrine and underwater settings, creating documentation requirements that cannot be addressed by a single sensing approach. This study presents the multi-sensor geometric documentation of eight cultural heritage sites. Unmanned aerial vehicle (UAV) photogrammetry was applied to six inland and coastal sites, while underwater photogrammetry, unmanned surface vehicles (USVs), acoustic sounding and a prototype green-wavelength flash LiDAR were used at three shallow-water sites. The campaigns produced orthomosaics, elevation models, dense point clouds, textured meshes, bathymetric maps and underwater LiDAR point clouds at scales appropriate to the conservation problem of each site. The resulting products document exposed architectural remains, excavation areas, cliffs and unstable slopes, lake-margin changes, submerged masonry, wooden structures and lakebed morphology. Their main contribution is the establishment of spatially explicit, site-specific baselines that provide measurable geometric and visual evidence for condition assessment, future repeat-survey comparisons and the spatial integration of environmental, archaeological and conservation information. The study demonstrates the operational and information complementarity of optical, acoustic and active ranging approaches, which address different documentation scales, environmental constraints and heritage targets, and provide distinct spatial evidence that can serve as potential inputs to subsequent digital twin and decision support applications. Full article

Reliable short-term position forecasting may provide anticipatory state information for collision-risk assessment, communication management, and prediction-assisted control in Internet of Things (IoT)-enabled unmanned aerial vehicles (UAVs); these downstream functions are not evaluated directly here. This study reformulates UAV position prediction as a flight-wise, multi-horizon, three-dimensional forecasting problem and tests whether position, velocity, gravity-resolved acceleration, and quaternion-orientation histories improve predictive accuracy while measuring model-level edge-inference cost rather than end-to-end system latency. The dataset contains 3100 AirSim flights with high-rate kinematic, inertial, attitude, pressure, and magnetic-field measurements under variable horizontal wind. The reported generalization is flight-disjoint within one AirSim domain; route/scenario disjointness and transfer to physical UAVs are not established. Signals are converted to a common navigation frame, gravity-resolved, low-pass filtered, resampled to 50 Hz, and partitioned by flight identifier before normalization and window construction. Each learned model receives 2 s of history and predicts the complete next 1 s trajectory, with errors evaluated at 0.1, 0.5, and 1.0 s. The sensor-enriched LSTM (LSTM-PVAQ) is compared under matched conditions with persistence, constant-velocity, constant-acceleration, extended Kalman filter, reduced-feature LSTM, GRU, temporal convolutional network (TCN), and compact Transformer baselines. LSTM-PVAQ achieved 3D RMSE values of 0.043, 0.168, and 0.371 m at 0.1, 0.5, and 1.0 s, respectively. At 1 s, its RMSE was 21.7% lower than LSTM-PV, 13.1% lower than GRU-PVAQ, 9.3% lower than TCN-PVAQ, and 16.8% lower than Transformer-PVAQ. Its one-second ADE and FDE were 0.216 and 0.339 m. On a Raspberry Pi 5 CPU using one FP32 thread and batch size one, median neural forward-pass latency was 0.88 ms, well below the 20 ms model-update interval. The results show that gravity-resolved inertial and orientation histories improve multi-horizon prediction, while TCN-PVAQ remains an attractive lower-latency alternative. Full article

In resource-constrained unmanned aerial vehicle (UAV) infrared image transmission, infrared small targets usually occupy only a small number of pixels, while conventional uniform resource allocation strategies fail to consider the semantic differences among image regions, resulting in inefficient resource utilization and loss of target-related information. To address this issue, this paper proposes a semantic-aware resource allocation method for infrared small-target detection. First, infrared images are divided into multiple grid cells, and the semantic importance of each cell is estimated based on preliminary detection results. Then, the cells are classified into different semantic levels, followed by differentiated bit allocation and region-wise reconstruction. Finally, a joint optimization problem of grid partitioning and resource allocation parameters is formulated and solved by the covariance matrix adaptation evolution strategy (CMA-ES) to optimize both detection performance and image reconstruction quality. Experimental results demonstrate that the proposed method achieves mIoU values of 0.6106 and 0.7445 at available bit rates of 12.5% and 50%, respectively, showing significant improvements over comparison methods. Moreover, CMA-ES obtains stable optimization results with fewer evaluations and provides a favorable balance between detection accuracy and reconstruction quality. These results indicate that the proposed method can effectively preserve target-related semantic information under limited communication resources, providing an effective solution for efficient UAV infrared image transmission and reliable small-target detection. Full article

This paper presents an adaptive outer-loop neural PID control scheme with asymmetric saturation and clamping anti-windup for a quadcopter drone, using online training based on the Extended Kalman Filter. This proposal mitigates the effects of actuator saturation and maintains operational feasibility under physical constraints. Multirotors possess distinct aerodynamic capacities, requiring continuous thrust for vertical gravity compensation versus tilt-induced forces for horizontal translation. Therefore, the proposed framework incorporates coupled asymmetric saturation limits to prevent directional vector distortion alongside a clamping-based anti-windup mechanism and dynamic tuning for controller gains. Full article

Methods for object localization and classification in images acquired from unmanned aerial vehicles (UAVs) quickly develop and find new applications. Pre-trained convolutional neural networks (CNNs) play the key role in solving these tasks. However, there are many factors that degrade the quality of acquired images and make the performance of methods intended for object detection and classification worse. One such factor is lossy compression of acquired images or video data widely used to pass them from on-board sensors and devices of preliminary data processing to on-land centers that perform further data processing for retrieval of valuable information. Both CNNs applied for localization and classification, and lossy compression techniques used to reduce the transferred data size have an impact on final results. To study this impact, we analyze the performance of several modifications of YOLO (You Only Look Once) CNNs applied to color images compressed by JPEG, which continues to be one of the basic compression tools. The quality factor is varied within wide limits to detect the situation when distortions due to lossy compression start to become too large and have a considerable negative effect on the localization and classification of objects of different types and sizes. Analysis is carried out using several traditional criteria, including Intersection over Union, F1, and mAP metrics, as well as some others. Dependence of localization and classification characteristics on the object size is performed. The datasets VisDrone and TAI are employed in training and verification. Full article

Large-area remote-sensing data provide essential pre-mission information for unmanned ground vehicles, but their spatial support and temporal latency may obscure local terrain changes. A remaining challenge is to translate heterogeneous regional evidence and recent local observations into a consistent, updateable, and planner-ready map. This study presents a satellite–unmanned aerial vehicle (UAV) workflow for constructing and incrementally maintaining an off-road traversability map for mission-level global planning. A common H3 index organizes satellite imagery, terrain, soil, road evidence, and local UAV semantic observations while retaining their native spatial support and provenance. The map separates environmental-prior, semantic, and traversability-cost layers to support interpretable fusion and independent updating. A confidence-hierarchical conflict resolution mechanism resolves inconsistencies in the regional prior, while an observer-agnostic interface projects UAV semantic observations onto local map cells. RGB imagery is used by the primary UAV observer, and digital surface model (DSM) is evaluated as an optional semantic-observation modality. Evaluation included a manually reviewed regional benchmark, a unified buffered spatial holdout, cell-level update assessment, and 40 fixed replanning tasks. Conflict resolution reduced high-risk omissions. RGB-only SegFormer-B2 achieved the highest semantic accuracy with moderate computational complexity. UAV override achieved a cell-level F1 score of 96.96% and limited the false-positive accumulation associated with conservative union. Replanning further revealed a trade-off between hazardous-cell avoidance and search-graph connectivity. The proposed workflow provides a maintainable interface between multi-source remote sensing and global UGV planning rather than a replacement for onboard perception, local obstacle avoidance, or vehicle control. Full article

Vision-based fire and smoke detection must be both accurate and lightweight for edge cameras and unmanned aerial vehicles (UAVs). Most lightweight fire detectors, however, are validated on a single dataset from a single run, which leaves the accuracy–efficiency trade-off and its external validity only partially examined. Rather than a new state of the art, we take an evaluation-centered stance and study two lightweight operating points of YOLO11n: an accuracy-first variant (LFS-YOLO11-A) that adopts the Wise-IoU (WIoU) loss at no extra parameters, and a lightweight variant (LFS-YOLO11-B) that further adds GhostConv. Both are evaluated on the public D-Fire dataset, retrained on a second dataset (DFS) over eight seeds, and profiled across GPU/CPU under PyTorch and ONNX Runtime. LFS-YOLO11-A matches the baseline on D-Fire (mAP@0.5 0.760 versus 0.758), while LFS-YOLO11-B reduces parameters by 12.4% and computation by 11%, both exceeding 160 FPS end-to-end (batch = 1) on a desktop-class GPU. On DFS, WIoU yields a small, exploratory +0.6-point improvement (nominal paired p = 0.037; seed-sensitive, with a confidence interval lower bound near zero), whereas an apparent +6.1-point gain from an early, uncontrolled run proved to be train/test contamination introduced before the data pipeline was frozen, not a genuine effect. Frozen-pipeline, multi-seed, two-benchmark evaluation is therefore necessary to separate genuine effects from the artifacts of uncontrolled single runs in lightweight fire and smoke detection. Full article

Unmanned aerial vehicles (UAVs) have become essential platforms for remote sensing in challenging environments such as high-dynamic-range (HDR) scenes and low-texture areas. However, conventional frame-based visual–inertial simultaneous localization and mapping (SLAM) systems often suffer from motion blur and overexposure during high-speed UAV flight, leading to state estimation failure. To address numerical instability in marginalization, weak scene adaptability, and insufficient outlier suppression in event-based stereo visual–inertial SLAM systems for aerial applications, we propose HESVI, a hybrid marginalization and adaptive heterogeneous kernel state estimation method for UAV remote sensing. Our method first establishes a focal-length-driven cross-modal inverse depth consistency constraint to couple image and event inverse depths, providing high-quality priors for optimization. Such lightweight prior generation is designed with the limited onboard computing resources of UAV platforms in mind. A hybrid marginalization strategy is then introduced, employing block-parallel tall–skinny QR (TSQR) acceleration based on Householder reflections alongside dynamic Tikhonov regularization and first-estimates Jacobian (FEJ) linearization to balance computational efficiency and numerical stability. Furthermore, an adaptive heterogeneous Cauchy kernel maps differentiated thresholds to image and event features according to their average effective tracking lengths, enabling dynamic outlier suppression. Experiments on the VECtor, MVSEC, and HKU datasets demonstrate that HESVI achieves the best absolute trajectory error (ATE) on the vast majority of the evaluated sequences, with average ATE reductions of 47.2%, 41.2%, and 26.8% over PL-EVIO, ESIO, and ESVIO, where each average is computed only over the sequences on which the corresponding baseline runs successfully. The method also exhibits excellent performance in complex remote-sensing scenarios and generalization tests. HESVI effectively enhances the numerical stability, scene adaptability, and localization accuracy of event-based stereo visual–inertial SLAM systems in challenging UAV remote-sensing environments. Full article

Coordinating multi-agent unmanned aerial vehicle (UAV) swarms is challenging in disaster scenarios where communications are dynamic, unreliable, and subject to UAV losses. While distributed artificial intelligence has enabled unprecedented levels of autonomous multi-agent coordination, most methods implicitly take communication topology as a given, not accounting for its limited maintenance in such scenarios. As a result, communication fragmentation can undermine autonomous mission progress during highly dynamic communications conditions. This paper proposes the Adaptive k-Truss-Constrained Agentic AI Framework (ATAC), an AI-based decentralized coordination framework for multi-agent UAV systems, which explicitly factors in graph-theoretic structural considerations during decision-making. The swarm is modeled as a graph, where an adaptive k-truss backbone is maintained during dynamic communication conditions to preserve triangle-based redundancy. Each agent acts as a graph-aware AI entity which bases its decentralized decisions on local information and descriptors of the communication backbone. A closed-loop evolutionary process is used to rebuild the backbone after significant communication link losses while UAVs make mission progress decisions based on information from the current backbone, enabling continuous adaption of the swarm structure to the communication state. The efficacy of the proposed framework is demonstrated through a comprehensive simulation campaign which includes communication link losses, UAV failures, adaptive truss selection, ablation studies, reward sensitivity analysis, and computational performance assessments. ATAC is compared to alternative graph-aware coordination approaches, showing consistent improvements in maintaining communication, preserving backbone structure, enabling triangle-based connectivity, and overall structural recovery while still achieving high-levels of mission progress during dynamic disaster response scenarios. The results highlight the effectiveness of explicitly tying AI-driven decentralized decision-making to maintenance of a graph-theoretic backbone structure for resilient UAV swarm coordination. Full article