Search Results (2,807)

Real-time wideband radio frequency (RF) spectrum monitoring is increasingly important for unmanned aerial vehicle (UAV) detection and RF surveillance. Low-cost software-defined radio (SDR) networks are attractive but constrained by limited instantaneous bandwidth per node, I/Q data transfer bottlenecks over USB 2.0, and multi-node computational overhead. This paper proposes a bandwidth-efficient FPGA-GPU heterogeneous architecture addressing these limitations. A hardware-efficient cell-averaging constant false alarm rate (CA-CFAR) IP core is deployed on the edge FPGA of each SDR node, forwarding only signal-containing intervals to reduce data transfer volume proportionally to the target duty cycle. Spectra from multiple nodes are stitched into a wideband view and processed in real time via a GPU-accelerated pipeline. The CA-CFAR IP occupies 16.3% of available LUTs with no BRAM and a fixed 10-cycle latency at 100 MHz. Experiments on a five-SDR testbed demonstrate an 88% data transfer reduction at a 10% duty cycle, 376 μs latency from signal acquisition to display-buffer preparation, 96.26% detection probability at −83.16 dBm (SNR ≈ 13 dB), and a 4.5× to 6.0× GPU speedup over CPU processing. These results support real-time wideband RF monitoring on resource-constrained SDR platforms. Full article

(1) Background: The digital traces of unmanned aerial vehicles (UAVs) are becoming increasingly important in criminal incidents, the violation of airspace and in military operations, thus making the reconstruction of the digital traces a critical task. But, current tools like DatCon, Autopsy and GRYPHON cannot recover telemetry when the flight logs are logically damaged, fragmented or partially deleted and don’t offer any quantitative measurement of the confidence of the recovered information. (2) Methods: A unified hardware-software complex, including a forensic workstation, a hardware write-blocker and SD/microSD/eMMC adapters; a set of software modules for extracting artifacts from files, structural parsing of DAT/BIN/CSV log, neural network reconstruction of missing telemetry using a two-layer LSTM architecture; a multi-source correlation module that combines flight logs, telemetry, media metadata and controller artifacts; a module, Confidence Score (CS), that computes a reliability measure in $[0,1]$; and a visualization module to generate a reconstructed trajectory on an electronic map. (3) Results: The complex has been tested on 105 flights on 10 different UAVs, 492 flight logs were gathered, 10,435 were the media item files and 624 GB was the amount of storage during acquisition. The carving stage recovers 98.7% of artifacts across the eight signature classes, the LSTM module recovers all five telemetry parameters with $R^2>0.99$ and a single-step horizontal position error of 6.8 m, which is reduced to 4.7 m after multi-source correlation (below the 5 m operational target consistent with consumer-GNSS precision); the dependence on gap length is described by the empirical growth law ${\epsilon }_{\mathrm{horiz}}\approx 4.84·G^{1.44}$ m; 46.8% of recovered records fall within the high-confidence band of $\mathrm{CS}\ge 0.8$; and the complex outperforms DatCon, Autopsy + DJI Analyzer and GRYPHON by 22–35 percentage points in end-to-end record recovery and by a factor of ∼2.6 in mean horizontal error (4.7 m vs. 12.4–18.7 m). (4) Conclusions: The combined write-blocked hardware acquisition, neural reconstruction of telemetry, and quantitative confidence index provides a forensically structured pipeline that fills an existing gap in UAV digital forensics; we note that technical reconstruction accuracy does not by itself confer legal admissibility, which remains a function of jurisdiction-specific evidentiary standards discussed in the Conclusions. Full article

The emergence of unmanned aerial vehicles (UAVs) introduces new opportunities for the design of intelligent and flexible transportation systems beyond traditional road-based logistics. This study investigates the integration of UAVs as an alternative transportation mode within industrial environments, focusing on the rapid delivery of critical spare parts in large-scale production facilities. A two-stage optimization framework is developed, combining demand pre-processing with a routing algorithm that determines fleet utilization and delivery schedules under operational constraints. The proposed framework utilizes a data pre-processing stage, which converts enterprise resource planning order records into delivery-ready item data, with a mixed-integer linear programming (MILP) routing model that assigns eligible spare parts to UAV trips and determines the use of a fixed fleet under payload, dimensional, service-time, and battery-related constraints. The approach is evaluated using real annual order data from a metal-industry plant, combined with simulated intra-day arrival profiles due to the absence of exact order-placement timestamps in the ERP records. The results indicate that UAV-based transportation can serve a substantial share of internal demand while achieving shorter delivery-response times for the modeled UAV layer under the simulated dispatch instances and significantly lower direct energy-related transportation costs compared with the existing pickup-based process. The results highlight the role of UAVs as a complementary transportation layer in controlled industrial networks, supporting the transition toward more responsive and intelligent future transportation systems. Full article

Electric power systems are expanding toward higher voltage levels, larger renewable-energy bases, denser urban substations, and increasingly complex transmission corridors. These trends make inspection more frequent and more demanding, while conventional manual patrols and single-robot deployments remain constrained by safety risks, limited coverage, endurance, and fragmented situational awareness. Multi-robot systems offer a promising pathway for electric power inspection by combining heterogeneous platforms, distributed sensing, coordinated planning, and human-supervised autonomy. This review synthesizes recent progress in multi-robot inspection for power transmission lines, substations, distribution networks, and related grid assets, with particular attention to transmission corridors and substations where heterogeneous cooperation is operationally valuable. Following a Sense–Think–Act framework, we organize the literature into three interconnected components: cooperative perception for spatial and semantic understanding of grid assets; collaborative planning and task allocation for large-scale, risk-aware inspection; and coordinated execution with human oversight in safety-critical, often energized environments. We highlight how unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), climbing robots, and fixed robotic stations can complement one another in inspection workflows, from wide-area patrol and defect localization to close-range verification and maintenance support. We also discuss persistent challenges, including electromagnetic compatibility, reliable localization near metallic structures, multimodal data fusion, battery endurance, communication robustness, minimum approach distances, cybersecurity, benchmark scarcity, and the need for assurance mechanisms that allow operators to understand, trust, and intervene in multi-robot decisions. Finally, we outline a roadmap for moving from isolated demonstrations toward deployable, human-centered, and grid-integrated multi-robot inspection systems. Full article

Low-altitude radar target recognition is important for intelligent airspace monitoring, unmanned aerial vehicle (UAV) supervision, airport bird-strike prevention, and low-altitude remote sensing. Reliable recognition remains difficult because birds, balloons, and UAVs often produce weak radar responses, share similar trajectory-level signatures, and are difficult to annotate at scale. To address these challenges, this paper proposes a physics-informed semantic prompt learning framework for few-shot low-altitude radar target recognition. The framework converts radar point-track and track measurements into structured textual prompts that combine statistical descriptors, radar-domain physical knowledge, and task-specific instructions. A partially fine-tuned Generative Pre-trained Transformer 2 (GPT-2) encoder is then used to extract semantic representations that preserve motion and scattering-related information. An adaptive feature aggregation module further weights informative hidden states across temporal positions and semantic levels, and a relation-based meta-learning network models query-support similarity for few-shot classification. Experiments on a real low-altitude radar dataset with four target categories, namely birds, balloons, small rotary-wing UAVs, and light rotary-wing UAVs, show that the proposed method consistently outperforms conventional machine learning, deep learning, and representative few-shot baselines. Under the 20-shot setting, it achieves mean 90.85% precision, 90.47% recall, and 90.63% F1-score. The results indicate that embedding radar physical semantics into language-model-based representation learning can improve sample efficiency and recognition robustness for low-altitude radar remote sensing. Full article

Accurate detection and counting of open cotton bolls from unmanned aerial vehicle (UAV) RGB imagery are essential for organ-level cotton phenotyping and field monitoring, but remain challenging in high-density cotton fields because open bolls are small, densely distributed, partially occluded, and visually similar to plastic mulch, branches, senescent leaves, shadows, and drip-irrigation belts. To address these challenges, this study proposes OpenBoll-YOLO, a lightweight small-object detector designed for open cotton boll detection and counting in UAV nadir-view images. A UAV RGB dataset was collected at the boll-opening stage in Alar, Xinjiang, China, covering two cotton varieties and two acquisition dates. Based on YOLOv11s, OpenBoll-YOLO integrates three task-oriented components: a multi-kernel small-object enhancement pyramid (MSOEP) to preserve shallow spatial details and strengthen multi-scale feature fusion, a cross-stage partial block with a dynamic mixing layer (C2DML) to improve local structural discrimination under complex backgrounds, and a lightweight mixed aggregation network (LMANet) to enhance contextual representation with reduced model complexity. On the independent test set, OpenBoll-YOLO achieved 86.7% precision, 84.5% recall, 85.6% F1-score, 92.9% mAP@0.5, 81.5% mAP@0.75, and 71.7% mAP@0.5:0.95, with only 3.1 M parameters and an inference speed of 86 frames s⁻¹. Compared with YOLOv11s, it improved mAP@0.5 and mAP@0.5:0.95 by 2.4 and 5.9 percentage points, respectively, while reducing the parameter count by 67.0%. Counting evaluation further showed that OpenBoll-YOLO reduced the mean absolute error from 11.98 to 10.12 bolls image⁻¹ and increased R² from 0.87 to 0.91. These results demonstrate that OpenBoll-YOLO provides an accurate and lightweight solution for dense open cotton boll detection and counting in high-density field conditions. Full article

Recently, deep learning has enabled unmanned aerial vehicles (UAVs) to detect human bodies in aerial imagery, which is of particular importance in post-disaster situations such as floods and storms. Yet progress in this domain remains constrained by a familiar obstacle: the shortage of annotated training data. Neural networks, while powerful, are highly sensitive to data volume and diversity. Existing augmentation strategies help reduce this gap but typically introduce only incremental novelty, especially with respect to viewpoint variation, thereby limiting dataset richness. In this work, we propose a complementary strategy that leverages three-dimensional human models reconstructed via photogrammetric techniques. By situating these models within a controlled rendering environment, we generate synthetic imagery across a broad range of elevations and camera angles—perspectives that are rarely captured in conventional UAV datasets. These additions are designed to increase both the variability and the resilience of the training corpus. To evaluate the contribution of this approach, a custom CNN deep convolutional neural classifier was trained and benchmarked on a UAV human vs. non-human patch dataset of 4000 baseline images (128 × 128 px; 2800 train, 600 validation, 600 test), expanded with 3000 photogrammetry-derived synthetic patches (balanced by class) to 7000 total images for the 3DG setting. The primary metric was classification accuracy on the held-out test set, consistent with patch-level evaluation practice; detection-style metrics such as AP/IoU were not applicable to this binary classification protocol. Averaged over five independent training runs, the proposed augmentation improved classification accuracy by 3.02 percentage points over the baseline (88.06 ± 0.97% → 91.08 ± 1.03%), with consistent gains in precision, recall, and F1-score. When combined with standard augmentations (rotation, translation, scaling, flipping), accuracy reached 95.21 ± 0.61%, a gain of 7.15 percentage points over the baseline. These results suggest that photogrammetry-based augmentation offers a practical and effective enhancement for UAV-based human detection pipelines where timely, reliable identification is critical. Full article

Unmanned Aerial Vehicles (UAVs) increasingly operate in missions requiring the simultaneous satisfaction of multiple objectives: reaching task locations, performing the correct service, and preserving sufficient onboard energy for continuous operation. Mission efficiency depends not only on task completion but also on managing the trade-off between service duration and battery recharging. This work proposes a Double Deep Q-Network (DDQN) policy for energy-aware UAV navigation on graph maps. The UAV must first collect the appropriate servicing tool from a depot node and then deliver it to the active failure node. At the same time, it autonomously decides when to interrupt the mission for recharging so as to ensure sufficient battery reserve throughout continuous operations, while minimizing task-servicing duration. The key contribution is an energy-aware reward based on a Dynamic Battery Threshold (DBT) computed from graph shortest-path distances to the nearest charging station, enabling a topology-aware recharge policy that is safer yet less conservative than a per-map tuned safety margin. Extensive Monte Carlo tests on increasingly complex graphs show that the proposed policy achieves a 100% task completion rate with always sufficient final battery to reach a charging node from the task node, while degrading less with map complexity and exhibiting greater robustness to stochastic battery dynamics than a pseudo-optimal baseline. Full article

When UAV(Unmanned Aerial Vehicle) carry out wildlife inspection for biodiversity protection, there are challenges such as low target, complex background, variable shape and serious occlusion, which lead to insufficient accuracy and a high misjudgment rate of the existing lightweight detection model. We propose an improved lightweight YOLOv8n model, which aims to achieve higher accuracy and more real-time animal target detection under the UAV platform. To address the issue of small target features being easily lost in the deep network, we introduce a dynamic upsampling convolution for accurate feature-aware upsampling, which can effectively reconstructs target details and suppress background noise. In order to enhance the feature discrimination ability of the model in complex environments, a convolution block attention mechanism was integrated in the model, and the key features of the target were adaptively focused through the channel–spatial dual attention mechanism. Finally, in order to improve the positioning accuracy in dense and occluded scenes, we used MPDIoU loss function to optimize the bounding box regression, and achieve more stable and accurate alignment by minimizing the vertex distance between the prediction box and the real box. Experiments on public data sets show that the detection accuracy and efficiency of the proposed model are significantly improved compared with the original YOLOv8n: the number of model parameters is reduced by 10.7%, the amount of calculation is reduced by 9.9%, and the inference speed is improved by 25%. In terms of comprehensive performance, our method achieved a mAP@0.5 of 96.4%, a mAP@0.5:0.95 improvement of 6.0 percentage points, and an F1 score of 93.5%, while also significantly reducing the false positive rate. Experiments on self-made aerial animal data sets further fully verify that the algorithm can achieve high-precision real-time animal target detection in the actual UAV platform. Full article

Fine-grained monitoring of aquatic vegetation in crab ponds is essential for regulating water quality, sustaining ecological balance, and optimizing Chinese mitten crab (Eriocheir sinensis) aquaculture. However, owing to the complex water environment, fragmented vegetation morphology, and the absence of dedicated annotated datasets, traditional remote sensing techniques struggle to achieve highly accurate semantic segmentation and classification. In this study, we construct the first unmanned aerial vehicle (UAV) multispectral dataset for crab pond aquatic vegetation, encompassing four species, Alternanthera philoxeroides, Vallisneria natans, Hydrilla verticillata, and Elodea nuttallii, with pixel-level annotations verified by field surveys across typical aquaculture sites in Jiangsu Province, China. Furthermore, we introduce the Multi-scale Frequency–Spatial Collaborative Network (MFSCNet), built upon a MedNeXt backbone and augmented with distributed modules, including Channel Reduction Attention, Spatial Frequency Selection, a spatial–frequency fusion module, and Mobile Graph Convolution that operate cooperatively across the encoder, skip connections, decoder, and output head. This design suppresses complex water-background interference, enhances vegetation texture representation, and preserves the spatial continuity of vegetation patches. Experimental results demonstrate that, with a lightweight parameter size of merely 19.38 M, MFSCNet achieves a remarkable mean Intersection over Union (mIoU) of 0.9044, outperforming various mainstream convolutional neural network (CNN) and Transformer-based architectures. This study not only provides a high-precision remote sensing technical framework for the accurate multi-class identification and quantitative assessment of aquatic vegetation in crab ponds but also establishes reliable data support for refined aquaculture management and aquatic ecological conservation. Full article

Aiming at the problems of the traditional Statistical Priority-Based Multiple Access (SPMA) protocol in multi-hop Flying Ad Hoc Networks (FANETs), such as single-dimensional queueing only according to priority, unreasonable First-In-First-Out (FIFO) scheduling, high timeout dropping probability of multi-hop forwarding packets, and insufficient utilization of channel opportunities, this paper proposes a multi-hop adaptive SPMA protocol (MA-SPMA) suitable for dynamic multi-hop scenarios. The protocol adopts the Neighbor-Priority Two-Dimensional Queueing (NPTQ) mechanism to store packets jointly according to the next-hop neighbor and priority. A Priority-Utility Dual-round Decision (PUDD) mechanism is designed: in the first round, candidate queues that meet channel load conditions are selected in parallel; in the second round, a utility function constructed by normalized delay, priority, and the end-to-end transmission success rate is used to select the optimal packet for transmission. Theoretical analysis shows that the time and space complexity of MA-SPMA are linearly related to the number of neighbor nodes, with controllable overhead, which is suitable for resource-constrained Unmanned Aerial Vehicle (UAV) platforms. In the MATLAB simulation environment, the Reference Point Group Mobility (RPGM) model is used to construct a multi-hop topology, and comparisons are conducted with two typical improved protocols for multi-hop networks: DCLS-SPMA and BiLSTM-SPMA. The results show that the proposed protocol can significantly improve the end-to-end transmission success rate and network throughput, with more obvious advantages in scenarios with a high proportion of multi-hop services. This paper provides an effective solution for Medium Access Control (MAC) protocol design in FANETs. Full article

Unmanned Aerial Vehicle (UAV)-based small object detection is a challenging computer vision task. It is constrained by two primary factors: UAV platforms have limited onboard computational resources, and high-altitude objects often have weak features that are easily overwhelmed by complex backgrounds. To address these challenges, we propose LUIM-YOLO. First, a Lightweight Multi-Scale Feature Enhancement (LMSFE) module integrates parallel multi-scale convolutions with attention to strengthen small and low-contrast object feature extraction. Second, an Adaptive Multi-Scale Bottleneck (AMSB) module enhances key semantic features of small objects and spatial correlation of medium-scale objects. Third, an Enhanced Cross-layer Compensation Feature Pyramid Network (ECC-FPN) constructs cross-level interaction pathways to improve small object position and scale perception. Experimental results on VisDrone2019 show that compared with YOLOv8n, LUIM-YOLO reduces parameters by 57% and improves mAP@50 by 12.9%. Additional full-validation-set PyTorch inference tests on NVIDIA Jetson Orin show that LUIM-YOLO achieves 88.19 ms/image in FP32, indicating a parameter-efficient accuracy-oriented design with edge deployment potential. Full article

We investigate secure data transmission in a unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) sensing network, focusing on maximizing multi-sensor uplink secrecy capacity under practical power constraints and severe co-channel interference. Due to the coupled signal-to-interference-plus-noise ratio (SINR) expressions and the non-smooth secrecy-rate function, the formulated power allocation problem is highly nonconvex and mathematically challenging. To efficiently solve this problem, we exploit a novel mathematical reformulation by introducing a smooth approximation of the secrecy metric and developing a computationally efficient optimization framework based on sequential quadratic programming (SQP) with analytically derived gradients. The main strength of this framework lies in its low-complexity, deterministic nature, which eliminates the need for computationally exhaustive search heuristics while guaranteeing fast, stable convergence to a Karush–Kuhn–Tucker (KKT) point. Furthermore, we incorporate a robust worst-case eavesdropper modeling approach to guarantee secure communication under severe adversarial conditions. Numerical results demonstrate that the proposed method significantly improves sum secrecy performance compared to conventional equal-power and baseline allocation schemes, proving highly scalable for real-time data collection in environmental monitoring, smart cities, and surveillance applications. Full article

In Unmanned Aerial Vehicle (UAV) object detection tasks, complex lighting conditions and variable weather render robust all-weather perception challenging when relying solely on the visible modality. Although infrared modalities can provide complementary information, the reliability of individual modalities is highly scene-dependent. Existing multimodal detection methods typically adopt static fusion strategies, which ignore spatial heterogeneity of modal reliability and under-explore spatial-frequency collaborative representation, thus limiting detection robustness in dynamic environments. To address these issues, this paper proposes a Dual-domain Enhanced Adaptive Fusion Network (DEAF-Net), with two core innovative modules to tackle the above challenges. First, the Dual Domain Progressive Refinement (DDPR) module mitigates feature degradation caused by poor imaging conditions via the joint design of frequency-domain learnable filtering and scale-aware contextual refinement in the spatial domain, effectively suppressing noise, enhancing textures, and yielding a purified feature basis for fusion. Second, the Consistency–Discrepancy Guided Fusion (CDGF) strategy leverages the selective scanning mechanism of VMamba to model consistent and differential patterns across modalities, dynamically generates local modal contribution maps for adaptive fusion, and integrates global scene prior via entropy weights for calibration. Extensive experiments on the DroneVehicle and VEDAI datasets show that DEAF-Net outperforms mainstream multimodal detection methods, achieving mAP@0.5 scores of 81.9% and 76.2%, respectively, while delivering improved robustness in low-light, dense fog, and sparse-category scenarios. Full article

Detecting small targets in Unmanned Aerial Vehicle (UAV) imagery is challenging due to low pixel coverage, complex backgrounds, and information loss during downsampling. Existing detectors lack explicit mechanisms for enhancing weak target signals. We propose GHF-DETR, a Transformer-based detector featuring three collaboratively designed modules. First, a Heterogeneous Multi-Path Convolutional Network (HMC) backbone uses partial convolution and gated linear units to reduce computational redundancy while maintaining discrimination of small-object features. Second, a Dynamic Multi-Scale Focusing (DMSF) module integrates learned offset alignment with multi-kernel depthwise convolutions for cross-scale feature fusion. Third, a High-Frequency Selective Preservation (HSP) downsampling module combines space-to-depth convolution with 2D Discrete Wavelet Transform (DWT) to compensate for information loss in both spatial and frequency domains. On VisDrone2019, GHF-DETR achieves 33.1% mAP@0.5 and 18.6% mAP@0.5:0.95 with 15.4 GFLOPs and 7.59 M parameters, improving over the DFINE-n baseline by 5.4% and 3.1%, respectively, with AP_S reaching 10.1%. Generalization is validated on NWPU VHR-10. These results demonstrate that GHF-DETR achieves a favorable accuracy–efficiency balance for efficient UAV small-object detection. Full article