Search Results (179)

Small object detection in uncrewed aerial vehicle (UAV) imagery is hindered by limited pixels, insufficient detailed information, and strong background interference, leading to weak feature representation and poor contextual modeling. To address these issues, we propose a multi-domain enhancement and cross-layer feature fusion detection Transformer (MDCL-DETR) with progressive feature processing. First, a multi-domain enhancement module (MDEM) based on CSP (cross stage partial) structure is proposed, which fuses spatial and frequency-domain features in a lightweight manner to enhance object detail and global structures while effectively distinguishing object features from background interference. Second, a cross-layer feature extraction module (CLEM) is introduced to aggregate multi-scale features across layers, alleviate information loss caused by downsampling, and preserve spatial details of small objects while integrating high-level contextual semantics. Meanwhile, a gated Mamba fusion module (GMFM) is proposed, which adopts the Mamba architecture for long-range dependency modeling of multi-scale features and integrates a gating mechanism to realize the dynamic weighted fusion of local details and global context, further improving feature discriminability and global modeling capability. Finally, a fine-grained enhancement module (FGEM) is designed, which leverages feature reorganization and adaptive feature extraction to reinforce and compensate fine-grained features. Extensive experimental results validate the effectiveness and generalization of the proposed method, achieving mAP${}_{50}$ scores of $54.1\%$ and $56.2\%$ on the VisDrone2019 and AI-TOD datasets. Full article

This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is the Visibility and Line-of-Sight Path Simplification (VLoSPS) algorithm, an algorithm-independent post-processing method that removes redundant waypoints through long-range axis-aligned visibility analysis while preserving path feasibility. VLoSPS is integrated with the Direction-Aware Path Planning Approach (DAPPA) to reduce angular deviations and improve directional continuity. The proposed framework is applicable to standard algorithms, including A*, Dijkstra, Breadth-First Search (BFS), and Depth-First Search (DFS), without modifying their internal search mechanisms. The main academic contributions comprise the formulation of a generalized post-processing architecture for UAV-derived occupancy maps, the introduction of a visibility-aware waypoint reduction strategy, and extensive validation using two synthetic maze datasets and three UAV-derived semantically segmented real-world datasets. On the Göttingen Maze Dataset, the VLoSPS and DAPPA pipeline reduced the average path lengths of A*, Dijkstra, BFS, and DFS by 5.42%, 9.46%, 10.44%, and 86.00%, respectively. The consistent improvements across real-world datasets demonstrate the effectiveness, computational feasibility, and general applicability of the proposed framework for UAV-assisted UGV path planning. The implementation code and benchmark resources developed in this study are publicly released to promote reproducibility and facilitate future research. Full article

Collaborative search of multiple uncrewed aerial vehicles (UAVs) is a critical technology for maritime rescue operations. To address the challenge posed by an unknown target motion direction, we present an innovative framework, “Dynamic Response-Intelligent Coverage,” and develop a multi-UAV collaborative search model. This study employs a hybrid methodology combining theoretical analysis and simulation optimization. By leveraging the geometric properties of logarithmic spiral (LS) curves, rigorous kinematic modeling and mathematical derivations were conducted to obtain the theoretically optimal solutions for single- and dual-UAV collaborative search. Furthermore, to address the traditional analytical methods’ “curse of dimensionality” issue through a strategy space search and adaptive adjustment mechanism, the genetic-optimization-based multi-UAV collaborative search strategy optimization algorithm (GA-MCSSO) is developed for scenarios involving three or more UAVs. Simulation results demonstrate that: (1) In the dual-UAV search scenario, the simulation optimization results closely align with the theoretically optimal solutions, with highly consistent convergence trajectories; (2) In multi-UAV search scenarios, Compared with SSB and GA-MCSSO-Seq, GA-MCSSO reduces the total coverage time by approximately 32% and improves the cumulative detection probability by approximately 18% under idealized spiral planning conditions. When evaluated under realistic constraints, the absolute improvement in total coverage time averages 0.1–0.2 s, with a maximum gain of nearly 1 s. The theoretical-simulation complementary framework established in this study provides a systematic solution for collaborative search from single UAV to multi-UAV scenarios. The methodology offers technical insights for multi-agent dynamic optimization problems and provides significant theoretical support for practical search operations. Full article

The ability of uncrewed aerial vehicles (UAVs) to hover, recognize, and localize ground targets is crucial for efficient and accurate intelligent low-altitude operations, such as material delivery, emergency rescue, and firefighting. This paper presents a technical solution for low-altitude UAV target recognition and search localization. The core algorithm is a RepViT-enhanced detection model, which integrates the Re-Parameterization Vision Transformer (RepViT) lightweight neural network with an efficient object detection framework, further augmented by the Convolutional Block Attention Module (CBAM) to improve detection accuracy. The search localization strategy implements a tiered approach for exploring nearby areas from the current position, assigning targets to priority tiers and visiting them in order of priority. Experimental results demonstrate that the RepViT-enhanced model achieves a mean average precision (mAP) of 98.58% on a custom emergency rescue dataset, improving real-time detection speed by two frames per second (18.70 FPS vs. 16.70 FPS for the standard YOLOv4 baseline). Thus, the proposed method effectively enhances both detection accuracy and speed, enabling better target search and localization in complex environments. The search strategy was validated through simulations, confirming its feasibility. Full article

Responsible resource development in Alberta requires the reclamation of disturbed lands to achieve equivalent land capability to pre-disturbance conditions. Vegetation assessments on reclaimed wellsites and oil sand exploration (OSE) sites currently rely on plots placed in areas deemed representative using professional judgement, which may introduce sampling bias. This study compared woody vegetation attributes derived from conventionally placed plots with those from randomly placed plots on certified reclaimed sites. Furthermore, increased sampling intensity was evaluated on a subset of sites. Site-level plot-based estimates were also compared with estimates from uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) and airborne laser scanning (ALS). Woody stem density and height estimates from random and judgment-based plots were generally comparable; however, on sites with spatially heterogeneous recovery, judgment-based placement tended to overestimate woody stem density relative to larger-area sampling. LiDAR data captured spatial patterns of woody vegetation but underestimated stem densities, particularly on high-density, clustered sites. Full article

Drone-based Urban Air Mobility (UAM) shows immense potential in urban logistics and emergency response; however, evidence regarding its systemic sustainability remains fragmented. In a systematic review using the PRISMA methodology, this study analyzes 301 core articles to construct an evaluation framework spanning environmental, economic, social, and systemic effectiveness dimensions. Given technical similarities, electric Vertical Take-off and Landing (eVTOL) findings are integrated to anticipate operational challenges. Results highlight a clear consensus: drone delivery is time-efficient in high-sensitivity scenarios, though noise, equity, and safety remain critical bottlenecks. Meanwhile, deep controversies persist across some dimensions. Environmental benefits are highly context-dependent, contingent on operating models, battery life cycles, and clean energy proportions from a Life Cycle Assessment (LCA) perspective. Economically, a mismatch between high costs and low willingness to pay (WTP) necessitates optimized pricing strategies. Socially, public acceptance is sensitive to the balance between perceived benefits and risks. Furthermore, systemic effectiveness depends on the coupling between vertiports and ground infrastructure. Concluding that sustainable drone-based UAM is a multistakeholder systemic endeavor, we urge future research to prioritize LCA, pricing strategies, public acceptance surveys, and integrated air-ground coordination to resolve controversies and foster sustainable systems. Full article

UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint–Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both static terrain and dynamic obstacles. The KSC strategy reduces search complexity through orthogonal slice-based sparse keypoint extraction and path caching reuse, thereby improving the efficiency of global path planning. On this basis, PPO-based local obstacle avoidance is activated only when safety thresholds are exceeded, while the remaining path is replanned globally after threat clearance, which confines avoidance computation to a local scope while preserving global path quality. Experiments in static mountainous environments show that KSC requires substantially less computation time than RRT* and Informed RRT* while maintaining competitive path efficiency, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. Hybrid navigation validation experiments further show that KSC-PPO achieves high mission success, low collision rates, and low avoidance overhead in dynamic mountainous environments. Experiments demonstrate that KSC-PPO decomposes exponential global search space into controllable linear subproblems, significantly enhancing efficiency while ensuring path quality, providing an effective solution for UAV navigation in complex terrain. Full article

In uncrewed aerial vehicle (UAV)-based ground observation and detection missions involving high-speed moving targets or low-light conditions, Time Delay Integration (TDI) cameras enhance image brightness through multi-stage charge accumulation. However, the imaging quality is susceptible to motion blur induced by platform vibrations and velocity mismatch. Based on TDI imaging technology, a TDI image degradation model for a UAV-based imaging platform is formulated. To address spatial blurring caused by platform vibration and velocity mismatch during TDI imaging, we propose a TDI image restoration algorithm based on sharpness-optimized partitioned Point Spread Function (PSF) estimation. The main innovation lies in the first application of partitioned PSF estimation combined with image sharpness optimization in TDI imaging. By formulating an accurate TDI image degradation model, spatial motion blur kernel estimation is transformed into an iterative search problem for partitioned optimal PSF. Solving for optimal sharpness yields the optimal PSF and corresponding local motion parameters, achieving image restoration. Simulation and experimental results demonstrate that the proposed algorithm in this paper effectively removes motion blur in TDI dynamic imaging, while suppressing artifacts and ringing, thus significantly enhancing image quality. Full article

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation ${\mathrm{R}}^2$ of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation ${\mathrm{R}}^2$ was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s ${\mathrm{k}\mathrm{m}}^2$) using freely available satellite imagery and open-source machine learning frameworks. Full article

Target tracking for uncrewed aerial vehicles (UAVs) demands both low-latency, real-time inference and robust, long-term temporal consistency. Current approaches often face a trade-off between efficiency and stability in practice. This tension is particularly pronounced in resource-limited UAV platforms: computationally heavy architectures can exceed onboard processing capacity and energy budgets, whereas overly lightweight models degrade temporal state fidelity—leading to cumulative drift under challenging conditions such as occlusion, motion blur, rapid scale variation, and cluttered backgrounds. To address this challenge, we propose SPM-Track, a lightweight yet temporally consistent tracking framework grounded in explicit state maintenance. It introduces a dual-loop judgment-calibration architecture comprising three coordinated components: (1) the content-aware state encoder, which employs input-gate modulation, selectively models temporal dynamics to suppress noise propagation into the state; (2) the hierarchical state manager enhances robustness against long-term occlusions and appearance variations by coordinating short-term state updates with a long-term reliable snapshot library via dual-path cooperation; (3) the adaptive feature recalibration module applies joint spatial-channel discriminative weighting before response map generation, effectively enhancing target distinctiveness and mitigating background clutter interference. Experiments on UAV123, DTB70, UAVTrack112, and LaSOT show that SPM-Track outperforms lightweight baselines and remains competitive with several Transformer-based trackers, demonstrating a favorable trade-off between edge-deployable efficiency and long-term robustness in UAV-based tracking. Full article

Medical drone delivery (MDD), defined as the use of uncrewed aerial vehicles to transport medical products, is an emerging technological innovation responding to persistent health supply chain challenges in rural and low-resource settings. Within sub-Saharan Africa, MDD systems have demonstrated large-scale success in improving key health outcomes, health supply chain efficiency, and reductions in medical product stockouts and wastage. However, the existing evidence base on the effectiveness of this technology is dominated by quantitative, performance-based evaluations, with limited emphasis on the community-driven mechanisms that shape such outcomes. Drawing on original qualitative research, this article presents a qualitative secondary analysis (QSA) of interview data collected as part of a larger case study on MDD in Madagascar. The QSA, guided by socio-technical systems theory, analyzes a subset of 18 interviews with 23 community-level stakeholders to understand how MDD affects healthcare services in remote regions of the country. Participants reported that MDD led to downstream healthcare improvements in vaccination coverage and malaria-related health outcomes. These improvements were enabled through four interconnected socio-technical mechanisms: (1) improved medical product availability through the mitigation of geographic and transportation barriers, (2) stabilization of vaccine and cold chain transportation, (3) building trust and healthcare-seeking behaviours through predictable service delivery, and (4) reduced physical, mental, and financial burdens experienced by healthcare workers. A final, cross-cutting theme emphasized was the criticality of MDD program continuity, with participants noting that operation disruptions or withdrawals risked reversing benefits and breaking communities’ trust in the health system. By centering lived realities, perceptions, and social processes, this article bridges the gap between predominantly quantitative evidence on MDD systems and the experiences of the communities they are intended to serve. Full article

Global biomass estimates suggest that plants hold 81% of the Earth’s 550 GT C, yet carbon stocks in non-forested and dryland ecosystems remain the largest source of uncertainty in the global carbon budget. Small uncrewed aerial vehicle (UAV) platforms are increasingly used to estimate aboveground biomass at landscape scales. We conducted a systematic review of the remote sensing literature to determine: (1) which plant traits and related remote sensing indicators were used to develop aboveground biomass models; (2) statistical approaches; and (3) the key sources of uncertainty among these methods and models. We found that tundra, dryland, and savanna ecosystems were most underrepresented in the remote sensing literature. Within our systematic review process, we found no consistent UAV sensor combination, platform, or workflow that improved the accuracy and reduced the uncertainty in aboveground biomass estimates. Machine learning and regression models resulted in similar uncertainty levels in shrubland and savanna ecosystems. Expanding allometric equation development in shrublands and savanna ecosystems could reduce uncertainty and improve aboveground biomass estimation. Improved reporting on UAV logistics and workflows would further strengthen comparability. Standardized and validated UAV methods for estimating biomass, carbon stocks, and fuel loads will be essential for producing consistent datasets and enabling robust future meta-analyses. Full article

Aerial docking is a crucial capability for extending the autonomy and functionality of uncrewed aerial vehicles (UAVs), yet practical and robust docking mechanisms remain underdeveloped. Mid-air recovery also enables flexible multi-UAV cooperation across diverse mission scenarios. To address the core challenge of achieving reliable and precise airborne rendezvous, this paper proposes a control-driven approach supported by a complementary mechanical design. A Nonlinear Model Predictive Control (NMPC) framework is developed for the follower UAV, incorporating a velocity-penalty strategy to ensure the smooth and accurate tracking of the leader UAV based on GNSS guidance during the rendezvous phase. In the terminal docking stage, alignment accuracy is further enhanced through vision-based pose estimation using an ArUco marker array mounted on the leader UAV. Building on these algorithmic components, an improved active V-shaped docking mechanism is introduced to compensate for the follower UAV’s pitch angle during engagement, providing robustness against residual alignment errors. The feasibility and performance of the proposed system are validated through static ground docking experiments of the mechanical module and AirSim dynamic simulations evaluating the autonomous docking controller. Full article

Grassland productivity and precise monitoring of animal herbage intake are key requirements for sustainable grazing management in Mediterranean upland systems. This study aimed to evaluate the potential of uncrewed aerial vehicle (UAV)-based multispectral vegetation indices (VIs) combined with machine learning (ML) algorithms to estimate forage biomass, quality parameters and daily herbage dry matter intake (HDMI) of grazing ewes at the paddock scale. The experiment was conducted in a managed ryegrass–white clover meadow–pasture in southern Italy, where four plots were grazed sequentially by lactating Sarda ewes during spring–summer 2025. Ground measurements included pre- and post-grazing biomass inside and outside exclusion cages, botanical composition and forage quality. Concurrently, UAV multispectral imagery has been acquired, from which several VIs were computed. Pearson’s correlations were used to explore relationships between VIs and forage variables, and five ML algorithms. Indices such as MCARI2, MTVI2, MTVI, MSAVI and OSAVI showed the strongest associations with biomass and quality traits, while support vector machine and neural networks provided the best prediction accuracies, particularly for HDMI (R² up to 0.91). The integrated UAV–ML approach proved effective in simultaneously capturing spatial variability of pasture productivity and animal intake, supporting the development of operational precision grazing tools for heterogeneous Mediterranean grasslands. Full article

This study examines residual low-order (linear and constant) phase errors in interferometric synthetic aperture radar (InSAR) when compact, high-frequency radar sensors are mounted on commercial uncrewed aerial vehicles (UAVs). Although higher carrier frequencies and shorter standoff ranges enable fine-resolution interferometry, the same characteristics—together with UAV platform instability—make the system highly vulnerable to motion-induced phase errors, which can significantly degrade or even invalidate DEM reconstruction. This paper first quantifies the admissible motion-error bounds for reliable multi-baseline phase-gradient estimation, and then introduces a post-autofocus correction scheme that estimates the residual linear term from the interferometric fringe frequency and refines it via an FFT-based correlation objective, while the constant term is calibrated using ground control points (GCPs). The method is validated through simulations of a 24 GHz UAV demonstrator. To the best of our knowledge, this work provides the first post-autofocus demonstration of linear-and-constant residual-error mitigation for UAV-based high-frequency multi-baseline InSAR. In the considered K-band setting, the proposed approach reduces the DEM error from 42 m to 0.2 m (≈98% improvement). Full article