Search Results (2,237)

Accurate wildlife detection in Unmanned Aerial Vehicle (UAV)-captured imagery is crucial for biodiversity conservation, yet it remains challenging due to the visual similarity of species, environmental disturbances, and the small size of target animals. To address these challenges, this paper introduces YOLO-WL, a wildlife detection algorithm specifically designed for UAV-based monitoring. First, a Multi-Scale Dilated Depthwise Separable Convolution (MSDDSC) module, integrated with the C2f-MSDDSC structure, expands the receptive field and enriches semantic representation, enabling reliable discrimination of species with similar appearances. Next, a Multi-Scale Large Kernel Spatial Attention (MLKSA) mechanism adaptively highlights salient animal regions across different spatial scales while suppressing interference from vegetation, terrain, and lighting variations. Finally, a Shallow-Spatial Alignment Path Aggregation Network (SSA-PAN), combined with a Spatial Guidance Fusion (SGF) module, ensures precise alignment and effective fusion of multi-scale shallow features, thereby improving detection accuracy for small and low-resolution targets. Experimental results on the WAID dataset demonstrate that YOLO-WL outperforms existing state-of-the-art (SOTA) methods, achieving 94.2% mAP@0.5 and 58.0% mAP@0.5:0.95. Furthermore, evaluations on the Aerial Sheep and AI-TOD datasets confirm YOLO-WL’s robustness and generalization ability across diverse ecological environments. These findings highlight YOLO-WL as an effective tool for enhancing UAV-based wildlife monitoring and supporting ecological conservation practices. Full article

Oriented object detection constitutes a fundamental yet challenging task in Artificial Intelligence of Things (AIoT)-enabled maritime surveillance, where real-time processing of dense visual streams is imperative. However, existing detectors suffer from three critical limitations: sequential attention mechanisms that fail to capture coupled spatial–channel dependencies, unconstrained deformable convolutions that yield unstable predictions for elongated vessels, and center-based distance metrics that ignore angular alignment in sample assignment. To address these challenges, we propose JAOSD (Joint Attention-based Oriented Ship Detection), an anchor-free framework incorporating three novel components: (1) a joint attention module that processes spatial and channel branches in parallel with coupled fusion, (2) an adaptive geometric convolution with two-stage offset refinement and spatial consistency regularization, and (3) an orientation-aware Adaptive Sample Selection strategy based on corner-aware distance metrics. Extensive experiments on three benchmarks demonstrate that JAOSD achieves state-of-the-art performance—94.74% mAP on HRSC2016, 92.43% AP₅₀ on FGSD2021, and 80.44% mAP on DOTA v1.0—while maintaining real-time inference at 42.6 FPS. Cross-domain evaluation on the Singapore Maritime Dataset further confirms robust generalization capability from aerial to shore-based surveillance scenarios without domain adaptation. Full article

Background: The integration of unmanned aerial vehicles (UAVs) into warehouse management is shaped by a broad spectrum of influencing factors, yet practical adoption lagged behind its potential due to scarce quantitative models of factor interdependencies. Methods: This study systematically reviewed academic literature to identify key factors affecting UAV adoption and explored their interrelationships using complex network and social network analysis. Results: Sixty-six distinct factors were identified and mapped into a weighted network with 527 connections, highlighting the multifaceted nature of UAV integration. Notably, two factors, i.e., Disturbance Prediction and System Resilience, were found to be isolated, suggesting they have received little research attention. The overall network is characterized by low density but includes a set of 25 core factors that strongly influence the system. Significant interconnections were uncovered among factors such as drone design, societal factors, rack characteristics, environmental influences, and simulation software. Conclusions: These findings provide a comprehensive understanding of the dynamics shaping UAV adoption in warehouse management. Furthermore, the open-access dataset and network model developed in this research offer valuable resources to support future studies and practical decision-making in the field. Full article

Unmanned aerial vehicles (UAVs) increasingly share low-altitude airspace with birds, making early distinguishing between drones and biological targets critical for safety and security. This work addresses long-range scenarios where objects occupy only a few pixels and appearance-based recognition becomes unreliable. We develop a model-driven simulation pipeline that generates synthetic data with a controlled camera model, atmospheric background and realistic motion of three aerial target types: multicopter, fixed-wing UAV and bird. From these sequences, each track is encoded as a time series of image-plane coordinates and apparent size, and a bidirectional long short-term memory (LSTM) network is trained to classify trajectories as drone-like or bird-like. The model learns characteristic differences in smoothness, turning behavior and velocity fluctuations, and to achieve reliable separation between drone and bird motion patterns on synthetic test data. Motion-trajectory cues alone can support early distinguishing of drones from birds when visual details are scarce, providing a complementary signal to conventional image-based detection. The proposed synthetic data and sequence classification pipeline forms a reproducible testbed that can be extended with real trajectories from radar or video tracking systems and used to prototype and benchmark trajectory-based recognizers for integrated surveillance solutions. The proposed method is designed to generalize naturally to real surveillance systems, as it relies on trajectory-level motion patterns rather than appearance-based features that are sensitive to sensor quality, illumination, or weather conditions. Full article

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s National Hydrography Dataset. The dataset includes images, primary keys, and ancillary geospatial information. We use a manually labeled subset of the images to train models for detecting rapids, defined as areas where high velocity and turbulence lead to a wavy, rough, or even broken water surface visible in the imagery. To demonstrate the utility of this dataset, we develop an image segmentation model to identify rivers within images. This model achieved a mean test intersection-over-union ($IoU$) of 0.57, with performance rising to an actual $IoU$ of 0.89 on the subset of predictions with high confidence (predicted $IoU$ > 0.9). Following this initial segmentation of river channels within the images, we trained several convolutional neural network (CNN) architectures to classify the presence or absence of rapids. Our selected model reached an accuracy and F1 score of 0.93, indicating strong performance for the classification of rapids that could support consistent, efficient inventory and monitoring of rapids. These data provide new resources for recreation planning, habitat assessment, and discharge estimation. Overall, the dataset and tools offer a foundation for scalable, automated identification of geomorphic features to support riverine science and resource management. Full article

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves $R^2>0.90$ and $NRMSE<0.08$ for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design. Full article

Accurate cropland classification within smallholder farming systems is essential for effective land management, efficient resource allocation, and informed agricultural decision-making. This study evaluates cropland classification performance using Red, Green, Blue (RGB) and multi-spectral (blue, green, red, red-edge, near-infrared) unmanned aerial vehicle (UAV) imagery. Both datasets were derived from imagery acquired using a MicaSense Altum sensor mounted on a DJI Matrice 300 UAV. Cropland classification was performed using machine learning algorithms implemented within the Google Earth Engine (GEE) platform, applying both a non-binary classification of five land cover classes and a binary classification within a probabilistic framework to distinguishing cropland from non-cropland areas. The results indicate that multi-spectral imagery achieved higher classification accuracy than RGB imagery for non-binary classification, with overall accuracies of 75% and 68%, respectively. For binary cropland classification, RGB imagery achieved an area under the receiver operating characteristic curve (AUC–ROC) of 0.75, compared to 0.77 for multi-spectral imagery. These findings suggest that, while multi-spectral data provides improved classification performance, RGB imagery can achieve comparable accuracy for fundamental cropland delineation. This study contributes baseline evidence on the relative performance of RGB and multi-spectral UAV imagery for cropland mapping in heterogeneous smallholder farming landscapes and supports further investigation of RGB-based approaches in resource-constrained agricultural contexts. Full article

Unmanned Aerial Vehicle (UAV) imagery has emerged as a critical data source in remote sensing, playing an important role in vehicle detection for intelligent traffic management and urban monitoring. Deep learning–based detectors rely heavily on large-scale, high-quality annotated datasets, however, collecting and labeling real-world UAV data are both costly and time-consuming. Owing to its controllability and scalability, synthetic data has become an effective supplement to address the scarcity of real data. Nevertheless, the significant domain gap between synthetic data and real data often leads to substantial performance degradation during real-world deployment. To address this challenge, this paper proposes a high-fidelity synthetic data generation framework designed to reduce the Sim2Real gap. First, UAV oblique photogrammetry is utilized to reconstruct real-world 3D model, ensuring geometric and textural authenticity; second, diversified rendering strategies that simulate real-world illumination and weather variations are adopted to cover a wide range of environmental conditions; finally, an automated ground-truth generation algorithm based on semantic masks is developed to achieve pixel-level precision and cost-efficient annotation. Based on this framework, we construct a synthetic dataset named UAV-SynthScene. Experimental results show that multiple mainstream detectors trained on UAV-SynthScene achieve competitive performance when evaluated on real data, while significantly enhancing robustness in long-tail distributions and improving generalization on real datasets. Full article

In recent years, unmanned aerial vehicles (UAVs) have become increasingly prevalent across diverse application scenarios due to their high maneuverability, compact size, and cost-effectiveness. However, these advantages also introduce significant challenges for UAV detection in complex environments. This paper proposes an efficient feature perception network (EFPNet) for UAV detection, developed on the foundation of the RT-DETR framework. Specifically, a dual-branch HiLo-ConvMix attention (HCM-Attn) mechanism and a pyramid sparse feature transformer network (PSFT-Net) are introduced, along with the integration of a DySample dynamic upsampling module. The HCM-Attn module facilitates interaction between high- and low-frequency information, effectively suppressing background noise interference. The PSFT-Net is designed to leverage deep-level features to guide the encoding and fusion of shallow features, thereby enhancing the model’s capability to perceive UAV texture characteristics. Furthermore, the integrated DySample dynamic upsampling module ensures efficient reconstruction and restoration of feature representations. On the TIB and Drone-vs-Bird datasets, the proposed EFPNet achieves mAP50 scores of 94.1% and 98.1%, representing improvements of 3.2% and 1.9% over the baseline models, respectively. Our experimental results demonstrate the effectiveness of the proposed method for small UAV detection. Full article

This study aims to enhance the security of unmanned aerial vehicles (UAVs) within the Internet of Drones (IoD) ecosystem by detecting and preventing Denial-of-Service (DoS) attacks. We introduce DroneDefender, a web-based intrusion detection system (IDS) that employs machine learning (ML) techniques to identify anomalous network traffic patterns associated with DoS attacks. The system is evaluated using the CIC-IDS 2018 dataset and utilizes the Random Forest algorithm, optimized with the SMOTEENN technique to tackle dataset imbalance. Our results demonstrate that DroneDefender significantly outperforms traditional IDS solutions, achieving an impressive detection accuracy of 99.93%. Key improvements include reduced latency, enhanced scalability, and a user-friendly graphical interface for network administrators. The innovative aspect of this research lies in the development of an ML-driven, web-based IDS specifically designed for IoD environments. This system provides a reliable, adaptable, and highly accurate method for safeguarding drone operations against evolving cyber threats, thereby bolstering the security and resilience of UAV applications in critical sectors such as emergency services, delivery, and surveillance. Full article

Traditional methods for assessing sunflower yield across large agricultural fields are typically labor-intensive and time-consuming. This study explores the integration of unmanned aerial vehicle (UAV) imagery and the YOLOv11 deep learning model for automated sunflower head detection and yield estimation. Aerial imagery was collected from sunflower fields using UAVs, and a YOLOv11-based detection model was developed to identify sunflower heads efficiently. Model performance was optimized by tuning the Confidence Threshold and Intersection over Union (IoU) parameters. A total of 1290 image tiles derived from 215 UAV images were used for model training and evaluation. The dataset was divided into training and testing subsets with an 80:20 ratio. The optimal configuration, achieved at a Confidence Threshold of 0.50 and an IoU Threshold of 0.40, yielded balanced and accurate results, including a Precision of 0.84, Recall of 0.95, mAP@0.5 of 0.95, and an F1-score of 0.90. The findings demonstrate that parameter adjustment directly influences model detection accuracy and reliability. Overall, this study confirms that combining UAV remote sensing with YOLOv11 offers a robust and scalable approach for automated sunflower yield estimation, significantly reducing manual effort and processing time. Moreover, the proposed framework can be adapted for other high-value crops, contributing to the advancement of intelligent and data-driven agricultural management systems. Full article

The proliferation of unmanned aerial vehicles (UAVs) poses escalating security threats across critical infrastructures, necessitating robust real-time detection systems. Existing vision-based methods predominantly rely on single-modality data and exhibit significant performance degradation under challenging scenarios. To address these limitations, we propose DCAM-DETR, a novel multimodal detection framework that fuses RGB and thermal infrared modalities through an enhanced RT-DETR architecture integrated with state space models. Our approach introduces four innovations: (1) a MobileMamba backbone leveraging selective state space models for efficient long-range dependency modeling with linear complexity $\mathcal{O}\left(n\right)$; (2) Cross-Dimensional Attention (CDA) and Cross-Path Attention (CPA) modules capturing intermodal correlations across spatial and channel dimensions; (3) an Adaptive Feature Fusion Module (AFFM) dynamically calibrating multimodal feature contributions; and (4) a Dual-Attention Decoupling Module (DADM) enhancing detection head discrimination for small targets. Experiments on Anti-UAV300 demonstrate state-of-the-art performance with 94.7% mAP@0.5 and 78.3% mAP@0.5:0.95 at 42 FPS. Extended evaluations on FLIR-ADAS and KAIST datasets validate the generalization capacity across diverse scenarios. Full article

Aiming to address the safety and stability issues caused by typical faults of Unmanned Aerial Vehicle (UAV) navigation sensors, a novel fault-tolerant method is proposed, which can capture the temporal dependencies of fault feature evolution, and complete the classification, prediction, and data reconstruction of fault data. In this fault-tolerant method, the feature extraction module adopts the FNKPCA method—integrating the Frobenius Norm (F-norm) with Kernel Principal Component Analysis (KPCA)—to optimize the kernel function’s ability to capture signal features, and enhance the system reliability. By combining FNKPCA with Support Vector Machine (SVM) and Bidirectional Long Short-Term Memory (BiLSTM), an active fault-tolerant processing method, namely FNKPCA–SVM–BiLSTM, is obtained. This study conducts comparative experiments on public datasets, and verifies the effectiveness of the proposed method under different fault states. The proposed approach has the following advantages: (1) It achieves a detection accuracy of 98.64% for sensor faults, with an average false alarm rate of only 0.15% and an average missed detection rate of 1.16%, demonstrating excellent detection performance. (2) Compared with the Long Short-Term Memory (LSTM)-based method, the proposed fault-tolerant method can reduce the RMSE metrics of Global Positioning System (GPS), Inertial Measurement Unit (IMU), and Ultra-Wide-Band (UWB) sensors by 77.80%, 14.30%, and 75.00%, respectively, exhibiting a significant fault-tolerant effect. Full article

Online visual object tracking is a critical component of remote sensing-based aerial vehicle physical tracking, enabling applications such as environmental monitoring, target surveillance, and disaster response. In real-world remote sensing scenarios, the inherent processing delay of tracking algorithms results in the tracker’s output lagging behind the actual state of the observed scene. This latency not only degrades the accuracy of visual tracking in dynamic remote sensing environments but also impairs the reliability of UAV physical tracking control systems. Although predictive trackers have shown promise in mitigating latency impacts by forecasting target future states, existing methods face two key challenges in remote sensing applications: weak correlation between trackers and predictors, where predictions rely solely on motion information without leveraging rich remote sensing visual features; and inadequate modeling of continuous historical memory from discrete remote sensing data, limiting adaptability to complex spatiotemporal changes. To address these issues, we propose TPC-Tracker, a Tracker-Predictor Correlation Framework tailored for latency compensation in remote sensing-based aerial tracking. A Visual Motion Decoder (VMD) is designed to fuse high-dimensional visual features from remote sensing imagery with motion information, strengthening the tracker-predictor connection. Additionally, the Visual Memory Module (VMM) and Motion Memory Module (M3) model discrete historical remote sensing data into continuous spatiotemporal memory, enhancing predictive robustness. Compared with state-of-the-art predictive trackers, TPC-Tracker reduces the Mean Squared Error (MSE) by up to 38.95% in remote sensing-oriented physical tracking simulations. Deployed on VTOL drones, it achieves stable tracking of remote sensing targets at 80 m altitude and 20 m/s speed. Extensive experiments on public UAV remote sensing datasets and real-world remote sensing tasks validate the framework’s superiority in handling latency-induced challenges in aerial remote sensing scenarios. Full article

Mapping coastal landforms is critical for the sustainable management of ecosystems influenced by both natural dynamics and human activity. This study investigates the application of Transformer-based semantic segmentation models for pixel-level classification of key surface types such as water, sandy shores, rocky areas, vegetation, and built structures. We utilize a diverse, multi-resolution dataset that includes NAIP (1 m), Quadrangle (6 m), Sentinel-2 (10 m), and Landsat-8 (15 m) imagery from U.S. coastlines, along with high-resolution aerial images of the Greek coastline provided by the Hellenic Land Registry. Due to the lack of labeled Greek data, models were pre-trained on U.S. datasets and fine-tuned using a manually annotated subset of Greek images. We evaluate the performance of three advanced Transformer architectures, with Mask2Former achieving the most robust results, further improved 11 through a coastal-class weighted focal loss to enhance boundary precision. The findings demonstrate that Transformer-based models offer an effective, scalable, and cost-efficient solution for automated coastal monitoring. This work highlights the potential of AI-driven remote sensing to replace or complement traditional in-situ surveys, and lays the foundation for future research in multimodal data integration and regional adaptation for environmental analysis. Full article