Search Results (11,034)

Unprotected left turns at urban intersections require drivers to continually regulate their driving behavior while negotiating conflicts with opposing through traffic. Existing studies have mostly examined such maneuvers through gap-acceptance decisions and surrogate conflict indicators, which cannot reflect how driving behavior changes for unprotected left-turn interaction events. To explore the evolution of unprotected left-turn interactions, this study develops a data-driven framework to decompose continuous left-turn trajectories into interpretable, variable-length driving primitives. Using high-resolution unmanned aerial vehicle trajectory data, 2490 valid left-turning and opposing-through vehicle interaction events were extracted. A Non-Homogeneous Hidden Markov Model was adopted to segment each interaction event into driving primitives. These primitives are then clustered using Time-Series K-Means with Dynamic Time Warping. The clustering results yielded six behavior patterns: cautious turning, low-speed waiting, accelerating departure, deceleration observation, intensive turning, and steady driving. These patterns were then mapped back to the temporal sequence of each interaction event to construct behavioral transition chains. The results demonstrate that an unprotected left turn typically evolves as an ordered combination of the identified behavior patterns, from waiting and observation to turning and departure, and that transitions among these patterns are associated with changing opposing-traffic conditions. Full article

Structural health monitoring and regular damage inspection are critical to ensure the operational safety of civil infrastructure and reduce life-cycle maintenance costs, while traditional manual inspection suffers from low efficiency, high subjectivity, and occupational safety risks for inspectors in hard-to-reach areas. Although existing reviews have explored image-based damage detection, most focus on single damage types or individual infrastructure categories, with few providing quantitative bibliometric mapping of the whole field. This study combines bibliometric analysis and systematic review to trace the development trajectory, identify unresolved technical bottlenecks and industry–academia gaps, and provide a structured reference for researchers and engineering practitioners. Following PRISMA guidelines, 171 peer-reviewed publications from the Web of Science Core Collection (2009–2025) were included after two rounds of screening (initial retrieval: 892 records). CiteSpace and VOSviewer were jointly used to analyze publication trends, institutional cooperation networks, and emerging research hotspots, followed by a systematic review of technical evolution and engineering applications. Results show that annual publications have maintained a growth rate of over 40% since 2019, with China (54.4%) and the United States (22.2%) as the core global contributors; 89.5% of research outputs come from universities and research institutes, while enterprise participation accounts for only 8.3%, indicating a clear technology translation gap. Technically, the field has evolved from traditional digital image processing to deep learning paradigms (CNN, YOLO, U-Net, GAN, Transformer), integrated with UAV platforms and 3D reconstruction to achieve both intelligent damage identification and 3D quantitative assessment. Key bottlenecks include scarcity of high-quality multi-class annotated datasets, poor model robustness in complex field environments, insufficient pixel-to-engineering scale conversion accuracy, and low model interpretability. Future directions include multimodal sensor fusion, unsupervised domain adaptation for real-world generalization, lightweight edge-deployable detection models, strengthened industry–academia collaboration, and explainable artificial intelligence to accelerate technology deployment in engineering practice. Full article

Early aerial smoke detection is vital for wildfire response, but deploying accurate two-stage deep detectors on resource-limited Unmanned Aerial Vehicles (UAVs) remains computationally prohibitive. Moreover, under uniform supervision, standard knowledge distillation struggles on aerial smoke data, where foreground–background imbalance is severe and smoke boundaries are visually ambiguous. To resolve this, we propose the Decoupled Topology Distance Distillation (DeTD) framework to compress two-stage smoke detectors for real-time edge inference. DeTD features three key innovations. First, a decoupling module uses ground-truth-derived binary masks to isolate smoke and background features, mitigating distillation class imbalance. Second, a topology distance distillation module projects these decoupled features onto a unit hypersphere, employing a novel Symmetric Triplet Loss. This jointly optimizes the intra-class compactness and inter-class separability of both the foreground and background relational geometry between the teacher and student networks. Third, prediction-head soft-label distillation transfers class-conditional knowledge, synergistically complementing the intermediate-feature distillation. Comprehensive experiments on the D-Fire benchmark and a custom aerial UAV dataset yield mAP50 scores of 67.4% and 70.6%, respectively. DeTD consistently outperforms thirteen recent distillation baselines, and the lightweight student attains real-time-compatible inference, narrowing the accuracy–efficiency gap and indicating feasibility for deployment on resource-constrained UAV edge hardware. Full article

Magnetic field (MF) technologies have been explored in agriculture since the 1930s, with research activity increasing markedly since 2016. However, they have not achieved mainstream adoption, partly because no MF-specific validated methodology exists for evaluating their effects under realistic field conditions. Unmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, thereby enabling, for the first time, the systematic evaluation and validation of MF treatment responses under open-field conditions. To realise this potential, however, a common evidential basis must first be established by identifying crop physiological variables that are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream evidence synthesis of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency (NUE)/nitrogen assimilation, above-ground biomass (AGB), leaf area index (LAI), and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R² = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The review revealed a complete absence of integration between the two research domains within the reviewed corpus, despite their strong biological and methodological compatibility. The proposed framework is conceptual and remains to be experimentally validated; it provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems. Full article

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. In addition, complex models often fail to meet the computational requirements of edge-device deployment. Based on electronic toll collection (ETC) gantry data, this study proposes a causal–semantic spatiotemporal forecasting framework for long-term traffic flow prediction with a 24 h forecasting horizon. First, conditional Granger causality analysis is used to construct a directed causal prior graph that characterizes traffic propagation relationships among expressway segments. Second, scenario-semantic priors generated by a large language model are introduced to describe atypical traffic conditions. Then, causal structural priors and scenario-semantic priors are integrated into a teacher model and transferred to a lightweight student model through response-level and feature-level knowledge distillation. Experiments using expressway data from Hubei Province, China, show that the proposed model achieves the best overall performance in the typical scenario and competitive performance in the atypical scenario. The results indicate that the proposed framework can provide day-scale decision support for expressway law-enforcement UAV pre-deployment and enhance the spatial intelligence of traffic emergency management. Full article

To address the problems of small fire-spot scale, blurred boundaries, complex backgrounds, and insufficient feature representation of weak targets in Unmanned Aerial Vehicle (UAV)-based early forest fire detection, a YOLOv8n-based forest fire detection model, termed TriRHC-YOLO, is proposed. The model first introduces Reparameterized VGG (RepVGG)Block into the backbone network to enhance the extraction capability of shallow local features. Subsequently, a Hierarchical Feature Attention (HFA) module is designed to collaboratively model fire-spot features from three levels, namely directional structures, local textures, and global semantics, thereby enhancing the network’s capability to discriminate fire targets and suppressing interference from complex forest backgrounds. Finally, a Cross Stage Partial Feature Fusion with Cascade Star Block (C2f-CStar) module is designed to improve the representation capability of the model for local structural information and weak salient fire-spot features under complex backgrounds through cascaded spatial feature reconstruction and a star-shaped multiplicative gating mechanism. In addition, a UAV-specific early forest fire detection dataset is constructed based on the FLAME and FLAME_VISION datasets, and experimental validation is conducted on this dataset. The experimental results show that the proposed TriRHC-YOLO outperforms several classical YOLO algorithms, including YOLO11n, YOLO12, and YOLO26, as well as six advanced YOLO-based improved models. The Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 reach 0.769, 0.848, and 0.608, respectively. The results of the ablation experiments further verify the effectiveness of the three designed modules. Moreover, the proposed model contains only 3.181 M parameters and achieves 168.251 Frames Per Second (FPS), demonstrating favorable real-time detection capability. Overall, the proposed method can effectively improve the detection accuracy of early weak fire targets and the background suppression capability under complex forest backgrounds, making it suitable for real-time UAV-based forest fire inspection tasks. Full article

Urban low-altitude unmanned aerial vehicle (UAV) navigation combines partial observability, building occlusion, wind disturbance, and continuous control. This study develops and evaluates HW-GA-SAC, a multimodal history-window Soft Actor-Critic (SAC) policy for procedurally generated three-dimensional MuJoCo cities. A Gated Transformer-XL (GTrXL)-inspired gated-attention encoder processes a fixed eight-step navigation history, while a current-frame safety branch supplies vertical clearance, sparse Light Detection and Ranging (LiDAR)-like range sectors, and handcrafted safety cues directly to the actor and critic. The policy uses obstacle-related observations and reward shaping to support collision avoidance; it does not include constrained policy optimization or a separate runtime safety filter. In a seven-method comparison using five training seeds and five evaluation layouts, HW-GA-SAC achieved a 96% ± 3% success rate, 207 ± 16 average return, and 3% ± 4% timeout rate. Feedforward SAC achieved 92% ± 11% success and a 7% ± 10% timeout rate, but its successful paths were more direct. Five-seed learning curves, city-split evaluation, wind sensitivity, sensing perturbations, inference profiling, and ablation studies further characterize the method. Within this simulation protocol, HW-GA-SAC provides the strongest completion-oriented performance, with a measurable trade-off between task completion and path directness. Full article

Reliable target detection and geographic localization are critical for unmanned aerial vehicle (UAV)-assisted search and rescue (SAR) yet remain challenging in complex outdoor environments. Small targets in UAV Red–Green–Blue–Infrared (RGB–IR) imagery suffer from background clutter, occlusion, low illumination, and infrared thermal diffusion, while localization is vulnerable to unstable viewpoints and terrain-induced ray uncertainty. This study presents an integrated UAV-SAR framework coupling lightweight multispectral detection with Digital Elevation Model (DEM)-constrained geographic localization. For detection, the Asymmetric Fusion and Context-aware Detection (AFC-Det) network leverages asymmetric dual-stream encoding, cross-modal mutual prompting, and high-resolution anchored aggregation to enhance small-target representation from RGB–IR pairs. For localization, the Global Context-Regularized Huber Ray Consistency Optimization (GCR-HRCO) improves geolocation via global ray aggregation, multi-ray geometric consistency, Huber robust optimization, and DEM-based terrain constraints. Experimental results demonstrate AFC-Det achieves 45.4% average precision (AP) and 44.7% AP for small objects (APs) on the VTSaR dataset, with 1.7 million parameters, 8.0 GFLOPs, and 107.2 FPS, generalizing well to M3FD (54.6% AP). On SAR-DAG_raycast, GCR-HRCO reduces mean horizontal error from 6.85 m to 3.31 m and RMSE from 8.16 m to 4.33 m. Collectively, these results demonstrate the effectiveness of the proposed detection and localization components. Full article

The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches. Full article

Jamming introduces unintentional disruptions in the system to exploit the legitimate communicating equipment. Clustered jamming considers jammers that are present in multiple groups to disrupt the intended communication. Vehicle-to-everything (V2X) transmissions are critical for smart transportation. This research considers full-duplex environment, featuring unmanned aerial vehicles (UAVs) and cellular-base-station-aided V2X (C-V2X) systems exploiting clustered jamming using 3-dimensional (3-D) beam-forming millimeter-wave antennas. UAVs are modeled as a 3-D Poisson point process (PPP), and macro-based tower-mounted base-stations (MBSs) are modeled as a 2-D PPP. Roads are modeled as a Poisson line process. The vehicular nodes (V-Ns) are modeled on each road as a 1-D PPP. The deviations of the UAV’s millimeter-wave band antenna beam follow a Normal distribution. In this paper, for a full-duplex setting, the probabilities of coverage and equipment-association, along with the efficiency of the spectrum associated with various UAV and tower-based connections, are explored in the presence of clustered jamming. The probability of coverage and association of multiple links is derived with respect to the jamming clusters, V-Ns, MBSs, UAVs, jammers’ power, and antenna beams. The results demonstrated that jamming degrades system’s efficiency. This efficiency is further degraded whenever higher 3-D beam-width deviations of the millimeter waveband antenna and jammers are present. Therefore, robust counter-scenarios should be designed for the cases where jamming signals and varying beams disrupt the network. Full article

Understanding the spatial structure of tree communities is fundamental for evaluating ecological interactions and management dynamics in agroforestry systems. However, the structural complexity and small spatial scale of traditional agroecosystems often limit the use of conventional remote sensing approaches. Recent advances in drone-based photogrammetry offer new opportunities to reconstruct the three-dimensional structure of vegetation at high spatial resolution and to quantify tree-level structural attributes. In this study, we applied aerial photogrammetry from unmanned aerial vehicles (UAVs) to characterize the spatial structure of agroforestry systems in traditional home gardens (THGs) in the Yucatan Peninsula, Mexico. The immediate neighborhood structure of the tree community of 20 THGs distributed along a south–north precipitation gradient was analyzed using two focal species as anchor references: Spondias purpurea and Annona muricata. High-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume, which were combined with field measurements of diameter at breast height. Spatial indices describing aggregation, dominance, and neighborhood diversity were calculated to evaluate tree spatial organization and potential interaction patterns. The UAV-derived structural metrics revealed significant differences in canopy architecture across regions and between focal species. Regardless of the focal species, trees in the southern region exhibited greater height, crown diameter, and canopy volume than those in the northern region. Moreover, the spatial arrangement of tree communities also differed depending on which focal species was considered as the anchor, suggesting contrasting strategies of canopy dominance and spatial coexistence. Finally, our results validate the use of drone-based photogrammetry as an effective approach for capturing fine-scale spatial structure in complex agroforestry systems. By enabling detailed three-dimensional reconstruction of tree canopies, UAV remote sensing offers an affordable, simple approach to investigate neighborhood interactions, management effects, and structural dynamics in traditional agroecosystems that are difficult to assess using conventional field- or satellite-based methods. Full article

Unmanned aerial vehicles have become indispensable components of modern defense systems by conducting Intelligence, Surveillance, and Reconnaissance (ISR) missions to collect critical operational information through onboard sensing technologies, supporting secure communication and information sharing among distributed military assets, and enhancing battlefield situational awareness through real-time sensing and data fusion. In addition, UAVs are increasingly capable of executing a wide range of defense missions, including target search and tracking, electronic warfare, search-and-rescue, and combat support. The integration of AI into UAV-based systems has the potential to enhance these operational capabilities by enabling intelligent perception, autonomous decision-making, adaptive mission planning, autonomous navigation, resilient communications, and cooperative multi-UAV coordination, thereby enabling the autonomous and collaborative execution of complex defense missions. This paper presents an up-to-date review of AI-enabled UAV-based defense systems, focusing on major operational domains including autonomous air combat and cooperative UAV operations, path planning and autonomous navigation, target tracking/detection/classification, cybersecurity, electronic warfare protection, and resilient UAV operation. In addition to surveying the recent literature, this paper provides an integrated system architecture, a functional classification framework, and an analysis of the AI paradigms enabling next-generation UAV-based defense systems. Furthermore, this review synthesizes the key technological trends, lessons learned, and cross-domain research challenges identified across the reviewed studies, providing a unified perspective on the current state of the field. Finally, this paper highlights promising future research directions for resilient, scalable, secure, and intelligent next-generation AI-enabled UAV-based defense systems. Full article

Low-aspect-ratio flying-wing unmanned aerial vehicles (UAVs) are attractive drone platforms for civilian remote sensing, environmental monitoring, infrastructure inspection, disaster assessment, and persistent public-service monitoring because their integrated tailless layout offers high aerodynamic efficiency and payload volume. A trajectory-command-based three-loop nonlinear dynamic inversion (NDI) control architecture enhanced by a nonlinear disturbance observer (NDO) is designed to address the critical challenges of rapid time variation, strong nonlinearity, strong coupling, and restricted yaw authority in low-aspect-ratio flying-wing UAVs. The core innovation lies in the development of a trajectory-command-to-attitude kinematic mapping mechanism, integrated with the NDO for active torque compensation of lumped uncertainties and time-varying external disturbances. Leveraging a mathematical model of a low-aspect-ratio flying-wing UAV standard model, a three-loop NDI controller comprising angular rate, attitude, and trajectory command loops was designed based on the time-scale separation principle. The NDO was further designed to estimate lumped disturbances and provide feedforward compensation, thereby establishing an NDI-DO system that mitigates the high sensitivity of conventional NDI to modeling inaccuracies. Simulation and robustness tests involving typical high-angle-of-attack maneuvers (e.g., Cobra and Split-S maneuvers) demonstrated that the NDI-DO system achieved a reduction in angular-rate tracking error by over 77.2% compared to the baseline NDI. Furthermore, the permissible range of aerodynamic parameter perturbations was improved by 23%, significantly enhancing tracking fidelity and disturbance rejection. In a 3-DOF wind tunnel free-flight test, the NDI-DO system achieved a substantial expansion of the controllable angle-of-attack (attitude-stability) envelope from 72.9° to 99.19°, substantiating the high reliability and engineering utility of the control framework in post-stall nonlinear regimes. These results indicate that the proposed NDI-DO framework can support safer envelope expansion, autonomous upset recovery, and robust flight control for civilian flying-wing drones operating under uncertain aerodynamic and environmental conditions. Full article

This paper addresses the urgent scientific and applied problem of automatic tracking of highly maneuverable Unmanned Aerial Vehicles (UAVs) using systems based on platforms with limited computing power (Edge Computing). The paper analyzes the shortcomings of classical correlation trackers and detectors based on deep neural networks when tracking targets with non-linear trajectories. A hybrid tracking method is proposed, combining the speed of a Kernelized Correlation Filter (KCF) and the accuracy of a neural network detector (YOLO11s). A key feature of the method is the developed algorithm for adaptive Kalman Filter correction, which utilizes a dynamic, scale-invariant Prediction Error metric as a trigger for motion anomaly detection. This allows the system to distinguish between measurement noise and sharp target maneuvers, executing an adaptive state reset using finite differences only at critical moments. Experimental validation on edge hardware (Raspberry Pi 5) using highly dynamic video sequences from the UAV123 and VisDrone datasets demonstrated that the proposed approach maintains an average processing speed of 18.89 FPS. By limiting deep neural network invocations to merely 2.71% of total frames, the algorithm successfully curtails thermal throttling while achieving a global Mean Root Square Error (RMSE) of 259.10 pixels across highly erratic trajectories. The method ensures high tracking reliability without a critical increase in computational load, making it highly suitable for use in autonomous embedded systems. Full article

Quarry waste piles are heterogeneous engineered embankments that are susceptible to slope instability, yet early detection of pre-failure surface changes remains challenging due to complex surface conditions and measurement uncertainty. This study presents an integrated remote sensing-based framework for monitoring quarry waste pile instability by combining multi-temporal change detection with scale-dependent surface roughness analysis. The original contribution of the proposed framework lies in linking displacement-based change detection with multi-scale characterization of surface roughness, enabling both observed surface movement and topographical conditions associated with developing instability to be evaluated within a unified monitoring approach. Multi-epoch Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) and photogrammetric point clouds were acquired before and after documented failure events at an active quarry site at active quarry sites located northeast of Montreal, Quebec, Canada. The regional climatic conditions, characterized by seasonal freeze–thaw cycles, rapid snowmelt, and periods of heavy rainfall, can promote water infiltration and elevated pore-water pressures, thereby increasing the susceptibility of these heterogeneous waste piles to slope instability. A standardized workflow was implemented, including precision alignment using a Recursive Iterative Closest Point (R-ICP) registration strategy, vegetation filtering with a multiscale CANUPO classifier, and uncertainty quantification through a Level of Detection (LoD) analysis. The resulting LoD thresholds were 10–15 cm for LiDAR-to-LiDAR comparisons and 34–36 cm for mixed-sensor datasets. Multi-scale roughness analysis revealed that zones which later experienced instability exhibited consistently higher and more heterogeneous roughness than adjacent stable areas within a well-defined linear scale range. A roughness-based A/D indicator enabled objective delineation of hazardous zones prior to failure. Post-failure monitoring showed surface smoothing following major displacement, followed by renewed roughness increases associated with secondary movements. These results demonstrate that scale-dependent roughness provides complementary information to displacement-based change detection, enabling potentially unstable areas to be identified and prioritized before substantial displacement becomes evident. The integrated framework can assist quarry managers in targeting field inspections and monitoring efforts toward higher-risk areas and support earlier preventive actions to reduce slope-failure risk. Full article