Search Results (11,050)

Wild arrowhead (Sagittaria trifolia L.) often occurs as isolated plants or compact clusters in paddy fields, yet these states are difficult to distinguish in unmanned aerial vehicle (UAV) imagery because they share similar color, texture, and leaf morphology. This study presents a two-stage framework in which an unchanged YOLOv8n detector localizes candidate targets and a dedicated Patch-cls network refines the single- or multiple-plant label. The classifier combines multi-level features, local multi-scale enhancement, and channel attention; a GhostConv variant is also evaluated to examine the efficiency trade-off. Annotation-box and detector-generated-box results are reported separately, followed by a complete-system evaluation that retains missed targets, false positives, duplicate detections, localization errors, and classification errors. Across three random seeds, the proposed Patch-cls obtained 94.00 ± 0.34% accuracy, 83.81 ± 1.19% Macro-F1, and 73.18 ± 3.91% multiple-class Recall. In the complete test pipeline, Macro-F1 increased from 0.5459 to 0.5539 and multiple-class F1 from 0.4224 to 0.4384, while mean average precision at an intersection over union (IoU) of 0.50 (mAP50) decreased slightly from 0.7023 to 0.7016. The optimized pipeline achieved 88.89 frames per second (FPS) on an NVIDIA RTX A4000 with approximately 1.62 GB peak allocated graphics processing unit (GPU) memory. The results indicate that Patch-cls can improve category balance under detector-generated crops, although the overall gain is modest and does not replace the need for stronger localization and dense-target separation. Full article

Object detection in unmanned aerial vehicle (UAV)-based visual sensing is important for aerial monitoring and intelligent perception. However, it remains difficult because camera-captured aerial images often contain small targets, cluttered backgrounds, occlusion, and limited edge-computing resources. We propose LFC-YOLO, a lightweight feature-complementary detector for small objects in UAV imagery. The main component of LFC-YOLO is the Tiny Object-Specific Detection Architecture (TSD-Arch), which removes redundant computation from deep layers and builds a shallow high-resolution feature pyramid to preserve localization cues for small targets. To reduce the extra cost introduced by high-resolution feature fusion, lightweight GSConv is integrated into the reconstructed neck. In addition, we embed a Feature Complementary Mapping (FCM) block into the C2f backbone structure and form a C2f-based Feature Complementary Mapping (C2f-FCM) module. This module combines semantic and spatial information and reduces interference from complex backgrounds. Experiments on VisDrone2019 show that LFC-YOLO improves the mean average precision at an intersection-over-union threshold of 0.5 (${\mathrm{mAP}}_{50}$) by 4.0 percentage points over YOLOv8s while reducing the number of model parameters by 73.9%. Additional evaluation on UAVDT shows that the proposed design remains effective across different UAV scenarios. Full article

Wildfires generate rapidly evolving hazard landscapes that disrupt ground-based logistics and render conventional disaster-response operations ineffective, motivating the use of unmanned aerial vehicles (UAVs) in civil-protection missions such as medical resupply, casualty search-and-rescue, and perimeter surveillance. Existing evaluations, however, share a common limitation: they assess performance using navigation-centric metrics—primarily success rate—without accounting for the temporal value of the mission objective. This paper characterises, within each planner family, how a single risk coefficient ρ governs the trade-off between navigation success and time-decaying mission value: holding each family’s algorithm and replan trigger fixed and sweeping only ρ isolates its effect, so that the risk setting maximising a family’s navigation success need not maximise its mission value. To study this, FLARE is introduced, a deterministic benchmark for fire-landscape adaptive risk evaluation, inspired by recent Greek wildfire events; all hazard dynamics (fire spread, structural collapse, moving obstacles, dynamic no-fly zones) are modelled as benchmark abstractions rather than incident-specific reconstructions. FLARE evaluates eight planners spanning six risk-handling paradigm families, isolating the risk coefficient by sweeping ρ within each family (algorithm fixed) with A* as the risk-blind static reference, and quantifies mission impact—medication efficacy, casualty survival, and data freshness—through a strictly time-decreasing mission-score function. The mission-value leverage of ρ is strongly family-dependent: decisive for the incremental soft-cost-inflation family—whose success-optimal ρ collapses its mission value (0.91 → 0.41)—strong for the worst-case (CVaR) family, moderate for the sampling family, and negligible for the reactive, hard-threshold and frequent-replan families. In some families the success-optimal and mission-optimal ρ diverge—a within-family ranking inversion—while in others they coincide; because the algorithm is fixed across each sweep, the divergence is attributable to ρ alone. Success of navigation is therefore a necessary but insufficient proxy for mission effectiveness, and the risk coefficient is a meaningful per-family parameter for mission-aware configuration. Full article

Predicting plantation establishment-failure prior to planting remains a major operational challenge due to the strong spatial variability in post-harvest environmental conditions. This study developed a spatially explicit modelling framework that integrated pre-planting unmanned aerial vehicle (UAV)-derived environmental, structural, terrain, and operational-treatment data to predict establishment risk across plantation landscapes. Environmental, terrain, vegetation, and structural predictors were derived from pre-planting multispectral UAV imagery, while plantation establishment outcomes were quantified approximately 21 months later using an automated tree-detection and assessment framework. The datasets were integrated within a ridge-regularised logistic regression model incorporating interaction terms, multi-scale predictors, operational treatment masks, and blocked spatial cross-validation. The model achieved strong predictive performance under within-site blocked spatial cross-validation, with moisture-related variables, vegetation condition, and structural metrics contributing most strongly to establishment-failure prediction. Predicted risk surfaces closely matched observed patterns of reduced stocking density and suppressed growth. Beyond predicting establishment-failure, the framework enables plantation managers to screen model-predicted outcomes under alternative treatment encodings before planting and to integrate the composite stocking-density and height response within a spatially explicit Establishment Index. The framework therefore demonstrates that future plantation establishment can be predicted from environmental conditions measured before planting and provides a scalable pathway for translating high-resolution UAV data into operational decision support. Full article

Plant breeding trials often involve a large number of genotypes, making field-based evaluation of agronomic traits labor-intensive, expensive, and time-consuming. Pre-harvest identification of superior genotypes using remote sensing could substantially improve breeding efficiency. This study evaluated the potential of unsupervised (clustering) and supervised (regression) methods based on unmanned aerial vehicle (UAV) multispectral imagery to differentiate winter durum wheat genotypes according to yield, grain protein content (GPC), and protein yield (PY). A three-year field experiment involving 26 genotypes was conducted at the Institute of Field Crops (Chirpan, Bulgaria). UAV data acquired at the end of flowering (BBCH 69) and the beginning of grain filling (BBCH 71) with a DJI Phantom 4 Multispectral were used to derive spectral vegetation indices (SVIs) and texture features (TFs). In the supervised approach, machine learning regression models were used to predict the target traits before grouping genotypes into low-, medium-, and high-performance classes, whereas Ward’s hierarchical clustering was applied directly to the UAV-derived features in the unsupervised approach. The resulting genotype groups were compared with genotype groupings based on the means and standard deviations derived from the field measurements. Both approaches successfully identified high- and low-performing genotypes for yield and PY, achieving accuracies of 50–100%. In contrast, both methods showed limited performance for GPC. ANOVA revealed that agronomic traits and the best-performing SVI, the Normalized Difference Red-Edge Index (NDRE), were strongly influenced by environmental variability, whereas TFs appeared to capture more genotype-specific structural characteristics. These findings demonstrate that both supervised and unsupervised UAV-based approaches can support early identification of superior wheat genotypes, with texture features showing particular promise for genotype discrimination across breeding trials. Full article

Three-dimensional (3D) path planning for Unmanned Aerial Vehicles (UAVs) presents a challenging multi-objective optimization problem that necessitates a balanced trade-off among path length, flight safety, and trajectory smoothness, especially in complex environments such as mountainous or hilly terrains. Traditional and even many meta-heuristic planning algorithms often suffer from premature convergence and suboptimal solution quality when navigating such intricate 3D spaces. To address these limitations, this paper proposes an Improved Dhole Optimization Algorithm (IDOA) that exhibits fast convergence and strong global optimization capabilities. The IDOA enhances the original DOA framework by integrating a logistic-map-based chaotic mapping, a dynamic chaotic perturbation mechanism, and an adaptive stage-division strategy. The algorithm is designed to address the 3D path planning problem for quadrotor UAVs, supporting typical flight maneuvers including climb/descent, obstacle avoidance, and smooth turning in simulated complex hilly terrain. A multi-objective fitness function incorporating path length, safety, and smoothness is designed, which constrains the optimization to generate collision-free, smooth paths that satisfy the quadrotor UAV’s dynamic maneuver constraints. Convergence curves confirm that IDOA significantly outperforms the original DOA in terms of convergence speed and final path optimality. Detailed experimental results show that compared to the original DOA, IDOA achieves a 6.31% improvement in minimum fitness values, a 9.7% reduction in average path length, and a 45.28% reduction in average path curvature‌ when compared to the baseline DOA. These consistent performance improvements demonstrate that IDOA provides an effective and robust solution for offline pre-flight 3D path planning in complex terrain, offering valuable technical support for autonomous UAV navigation in practical application scenarios such as terrain surveying and disaster search and rescue. Full article

Accurate discrimination between rice and artificial grassland remains challenging in regional agricultural monitoring because both herbaceous types share similar spectral signatures during vegetative growth, and existing land-cover products do not treat artificial grassland as a separate class. Using Jurong City, Jiangsu Province, as the study area, we propose a framework that optimizes red-edge vegetation index selection within crop-specific phenological windows to separate rice from grassland. Using Unmanned Aerial Vehicle (UAV) multispectral imagery and Sentinel-2 satellite data, we quantified spectral separability across eight phenological stages using Fisher ratios. We identified two optimal discrimination windows: early tillering (mid-June) and heading–flowering (early September). Within the heading–flowering window, a dual-index classification rule combining Normalized Difference Red-Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI) was transferred from UAV to Sentinel-2 and used to produce a 10 m rice–grassland map for the entire city. Spatial agreement with two publicly available rice datasets reached 75.2% and 79.5% for rice pixels, reflecting differences in spatial resolution, reference year, and class definition rather than classification error. Independent field validation using 200 samples yielded an overall accuracy of 92.50% (F1-score = 0.93), confirming the effectiveness of the VI–window optimization strategy. The framework offers an interpretable, physiology-driven alternative for crop-type mapping that relies solely on widely available multispectral bands. Full article

This paper addresses the problem of autonomous landing of a quadrotor unmanned aerial vehicle (UAV) on a moving ground vehicle subjected to unknown vertical oscillations generated by road irregularities. The proposed approach considers simultaneous longitudinal, lateral, heading, and altitude regulation in the presence of nonlinear coupled dynamics, aerodynamic effects, and platform motion disturbances. A nonlinear control architecture is developed by combining backstepping techniques, super-twisting sliding-mode control, and an adaptive internal model regulator. The longitudinal, lateral, and heading subsystems are stabilized through a block backstepping–sliding-mode framework, whereas the altitude subsystem is regulated using an adaptive internal model controller capable of compensating unknown multi-frequency oscillatory disturbances without prior knowledge of their amplitudes or frequencies. The complete UAV dynamics are derived from the Newton–Euler formulation, including aerodynamic forces, gyroscopic effects, and coupled translational–rotational dynamics. To improve robustness and avoid algebraic differentiation, exact first-order differentiators based on the super-twisting algorithm are incorporated into the control implementation. The proposed adaptive regulator is compared against a robust super-twisting sliding-mode altitude controller under low- and high-frequency oscillatory platform motions. Simulation results demonstrate that the adaptive internal model regulator achieves accurate trajectory tracking and consistently lower accumulated tracking errors than the robust super-twisting sliding-mode controller under both low- and high-frequency platform oscillations. These results highlight the suitability of adaptive output regulation techniques for autonomous UAV landing operations under oscillatory platform conditions with measurement noise. Full article

Low-altitude unmanned aerial vehicle (UAV)-integrated sensing and communication (ISAC) channels are governed by rapidly evolving multipath. These dynamics arise from UAV motion, air–ground geometry, and scene-dependent scatterers, yet field evidence comparing monostatic and bistatic sensing links remains limited. We develop a cooperative monostatic–bistatic measurement system for low-altitude UAV ISAC channel sounding. The system integrates a UAV-borne sensing node, a ground node, synchronized acquisition, and an offline processing chain. It enables UAV-borne monostatic sensing and air–ground bistatic sensing to be measured within the same low-altitude urban scenario. A field measurement campaign is conducted along a representative route containing buildings, trees, roadside facilities, and open ground. From the measured in-phase/quadrature (IQ) data, the system extracts channel impulse responses (CIRs) and power delay profiles (PDPs) as primary measurement products. Delay–Doppler processing, multipath-component extraction, and trajectory analysis are applied to compare link-dependent propagation behavior under matched environmental conditions. The measurement results demonstrate that the proposed platform can jointly capture monostatic and bistatic ISAC channel responses and support controlled and synchronized low-altitude UAV channel measurement experiments. This system provides an experimental basis for UAV ISAC channel modeling, measurement-platform assessment, and subsequent sensing algorithm evaluation. Full article

Platform-induced synthetic aperture is an effective approach to enhancing azimuth resolution in forward-looking radar imaging. However, for small platforms such as automobiles and unmanned aerial vehicles, the large volume of echo data required under continuous sampling, combined with the presence of Doppler ambiguity, poses substantial challenges for high-resolution imaging. To address these issues, this paper proposes a forward-looking FMCW radar imaging method based on sparse sampling intervals. A uniform linear array is first employed to acquire measurements at different platform positions, and an initial range-angle image is obtained for each channel. Adaptive beamforming is then applied to impose nulls on false-alarm regions, including grating lobes and left-right ambiguity. Finally, coherent accumulation across channels yields a high-resolution range-angle image. Simulation and experimental results demonstrate that the proposed method achieves high-resolution forward-looking imaging while significantly reducing the volume of echo data. Full article

Small-object detection in unmanned aerial vehicle (UAV) imagery remains challenging because target objects often occupy only a few pixels, exhibit weak feature responses, and are easily obscured by complex backgrounds. These aspects significantly limit the effectiveness of end-to-end detection systems. To overcome these limitations and enhance the detection accuracy in challenging UAV settings, this paper proposes MPC-DETR, a Multi-scale Patch Context Transformer that is based on RT-DETR. To begin with, a Local-Global Attention Fusion Module (LGAF) is proposed to capture fine-grained local features and long-range semantic relations of small objects. LGAF enhances feature representation through a lightweight multi-branch synergistic attention mechanism while introducing limited computational overhead. Second, a Dilated Context-Aware Feature Interaction Module (DCFI) is proposed to enhance the discriminative capability of high-level features in cluttered backgrounds and densely populated small-object scenes. DCFI allows more efficient feature aggregation and contextual comprehension through multi-scale contextual modeling and scale-adaptive feature interaction. Third, a Patch-Guided Multi-scale Feature Fusion Module (PGMFF) is developed to create a patch-guided contextual fusion approach that combines shallow, high-resolution features with deeper semantic information. This process improves the maintenance and representation of fine object information and minimizes information loss in feature propagation. The experimental results on the VisDrone2019 dataset show that MPC-DETR has an mAP50 and mAP50–95 of 52.5% and 33.3%, respectively, which are 4.6 and 4.0 percentage points higher than the baseline model. Further analyses of the UAVDT and HIT-UAV datasets also support the high generalization potential of the suggested method to various UAV-based small-object detection problems. In general, the findings suggest that MPC-DETR provides precise, strong, and efficient small-object detection in complicated UAV images. Full article

Carrier-based fixed-wing UAV landing is challenged by deck motion, carrier airwake, gust disturbances, strong trajectory–attitude coupling, and actuator constraints. To address these issues, this paper proposes a Predefined-Time Direct Lift/Side-Force-Integrated Approach Landing (PTDIAL) method. An integrated direct-force architecture is constructed using the trailing-edge flap for direct lift and the spoiler for direct side force, thereby reducing the dependence of trajectory correction on angle-of-attack- and bank-angle/sideslip-mediated regulation. A preview-based reference glide slope is generated from the predicted Ideal Touch Point (ITP) sequence to improve the response to deck motion. Predefined-time control laws are developed for the cascaded position, trajectory, attitude, angular rate, and velocity loops, with prescribed-performance constraints imposed on the attitude response. A predefined-time disturbance observer is introduced to estimate the lumped aerodynamic disturbances, while an auxiliary anti-saturation mechanism compensates for the effect of trailing-edge flap saturation. Lyapunov analysis establishes the practical predefined-time stability of the closed-loop system under bounded disturbances and actuator constraints. Various simulations demonstrate that the proposed architecture improves lateral and vertical tracking while preserving the UAV attitude, and Monte Carlo simulations further confirm the robustness of PTDIAL. Full article

Unmanned aerial vehicle (UAV) imagery can support plot-scale crop phenotyping, but spectral, RGB and structural predictors may contribute differently to different traits. We compared six predefined feature groups for predicting soybean SPAD and plant height (PH) in a 1.3 ha field experiment in Sanya, China. The field contained 6197 soybean planting plots, of which 234 had paired SPAD and PH measurements. Multispectral bands, vegetation indices (VIs), RGB descriptors and digital surface model (DSM) metrics were extracted from DJI Mavic 3 Multispectral imagery. Six regression algorithms were evaluated using random fivefold cross-validation, spatial block cross-validation and nested spatial cross-validation. Under random cross-validation, ExtraTrees with multispectral bands, VIs and RGB descriptors produced the numerically highest SPAD performance (R² = 0.589; RMSE = 6.66), while BayesianRidge with multispectral bands, VIs and DSM metrics produced the highest PH performance (R² = 0.760; RMSE = 7.14 cm). Nested spatial cross-validation yielded R² = 0.473 and RMSE = 7.56 for SPAD and R² = 0.690 and RMSE = 8.13 cm for PH. G4 was selected in four of the five outer folds for SPAD, although the selected algorithm varied, and G5 was selected in all five outer folds for PH. VIs improved prediction of both traits relative to the original bands. Adding RGB descriptors produced only a small and model-dependent improvement for SPAD, whereas adding DSM metrics produced a larger and more consistent improvement for PH. The complete feature set did not outperform G4 for SPAD or G5 for PH. The retained models were applied to all 6197 plots to map SPAD, PH and their field relative combinations. Because all of the validations used one field and one UAV acquisition date, the results describe performance within this experiment and do not establish transferability to other sites, years or growth stages. Full article

Small-object detection in unmanned aerial vehicle (UAV) imagery is hindered by limited target pixels, dense spatial distributions, background interference, and scale-sensitive bounding-box regression. This study develops a lightweight YOLO11n configuration in which established SCSA recalibration, DySample reconstruction, decoupled prediction, and SimOTA assignment act at successive stages of the detection pipeline. Its principal methodological contribution is a gradient-adaptive WIoU–NWD objective that uses previously observed regression-gradient fluctuations to balance overlap-oriented and distribution-based localization without altering the inference graph. On the official VisDrone2019-DET test-dev server, the 2.48 M-parameter model achieves 45.9% mAP@0.5, 27.0% mAP@0.5:0.95, and 20.2% AP_small, improving the YOLO11n baseline by 2.3, 1.4, and 2.4 percentage points, respectively; it also improves mAP@0.5/mAP@0.5:0.95 by 2.2/1.3 points on the vehicle-focused UAVDT benchmark and reaches 40.2 FPS on a Jetson Orin NX in 15 W mode using TensorRT FP16. The two benchmarks mainly represent urban, traffic, and low-altitude surveillance imagery; consequently, the cross-dataset result supports transfer within these conditions rather than universal generalization to all UAV applications. These results support a compact single-pass accuracy–efficiency trade-off for resource-constrained UAV perception, while the modest margin over fixed loss weighting indicates that the adaptive mechanism should be interpreted as an incremental, primarily small-object localization improvement rather than a complete solution to regression instability. Full article

Risk assessment of Unmanned Aerial Vehicle (UAV) path planning is a crucial step towards ensuring a safe UAV operation. However, achieving a reasonable risk assessment of UAV flight paths remains challenging because it involves multiple responsibilities and processes that extend beyond a single agency or organization. Additionally, it must balance various complex factors and data from social, technical, political, and economic sources. Most existing works on flight path planning evaluate flight risks at a global level and generalized geometric representations of the UAV operating environment with respect to the current applicable UAV regulations. However, geometric information alone does not provide sufficient insight for comprehensive and safe path planning. Therefore, there are other ideas and concepts for using semantic data to characterize objects, obstacles and actions in the UAV environment. Incorporating semantic information into path planning enables more meaningful scene descriptions and a better representation of relevant constraints, obstacles within UAV environment that may influence the safety level of UAV operation, and the relevant risk assessment process. In this paper, we propose the development of methods and frameworks integrated into a flight planning prototype designed to generate safe two-dimensional UAV routes within a local, fine-grained planning context. The prototype incorporates safety considerations to enable a comprehensive assessment of UAV operational risks. It leverages both geometric and semantic datasets to characterize objects and obstacles within the UAV environment. These datasets are processed and stored in a relational database to support structured access and long-term usability. All concepts and experiments were implemented using datasets from a study area in the city of Brunswick, Germany. Full article