Search Results (584)

Ship semantic segmentation based on unmanned aerial vehicle (UAV) imagery has important application value in maritime scenarios such as marine surveillance, port management, and maritime safety. However, UAV images often contain large scale variations of ships, a high proportion of small targets, and complex background interference, including sea surface reflections, waves, and clouds. These factors make accurate segmentation and boundary localization difficult. To address these issues, this paper proposes a UAV-based ship semantic segmentation network, termed MPFT-UNet. The network introduces a Multi-scale Dynamic Sparse Cross-gating (MDSC) module to improve the representation of small targets. A Boundary Supervision Refinement (BSR) module is used to enhance boundary delineation. In addition, a Transformer-based Feature Fusion (FFT) module is applied at the bottleneck layer to strengthen global semantic representation. Experimental results show that MPFT-UNet achieves better performance than existing methods across multiple evaluation metrics. The model obtains an IoU of 0.8365, Dice coefficient of 0.9028, Recall of 0.8881, and AP of 0.95731. These results indicate stable segmentation performance under complex maritime conditions. Compared with the baseline U-Net model, the IoU is improved by approximately 5.1%. Full article

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes. Full article

The year 2025 saw the continuing trend of worsening wildfire severity and impact with escalating costs, burnt area and casualties. Subsequently, the capability for a rapid response operation is ever-growing, with aerial assets providing a key role in fulfilling this function. One problem with aerial suppression is the reliance on updated fire data and precise fire front information. Drones or other long-endurance vehicles are commonly used to assist in this matter, providing real-time data and imagery to the manned suppression bombers. The interactions and collaboration between these systems to achieve an improved wildfire suppression can be classified as a system-of-systems (SoS). To facilitate the design, interaction and communication of the surveillance drones and suppression aircraft, this paper develops a holistic framework using an agent-based simulation. The framework allows for the analysis of top-level drone design parameters and operational considerations with their communication and collaboration both with each other and the suppressive agents. The results showcase the importance of swath radius for better wildfire coverage and suppression, with radii less than 50 m preventing successful exploration of the fire. The importance of monitoring is highlighted by the observed greater reductions in burnt area and fleet energy usage when increasing the monitoring agent fleet size by 50% compared to the same increase in suppression agent fleet size. Full article

A space-based bistatic passive radar system, typically utilizing a satellite as the illuminator of opportunity and ground or aerial platforms as receivers, offers significant advantages for wide-area maritime surveillance, robust anti-jamming performance, and superior survivability. However, due to the limited transmit power and significant path loss over long-range propagation, the signal-to-noise ratio (SNR) of sea-surface targets is extremely low. To achieve effective detection and estimation, long-time integration is required, which can unfortunately induce severe range cell migration (RCM) and Doppler frequency migration (DFM) effects, resulting in integration gain loss and degraded detection performance. This article proposes a hybrid integration and parameter estimation algorithm based on the keystone transform and segmented matched filtering (KTSMF), which partitions the echoes into multiple frames and combines the keystone transform with segmented matched filters for integration. It not only effectively eliminates RCM and DFM effects in both intra-frame and inter-frame processing but also addresses Doppler ambiguity and Doppler aliasing effects, which enables a generalized processing capability for slow-moving, fast-moving, and highly maneuverable targets. Simulation results and analysis demonstrate that the proposed method achieves superior detection performance and parameter estimation accuracy compared to existing algorithms. Full article

This work examines the aerodynamic efficiency improvement achieved by integrating C-type winglets into a small-scale Blended Wing Body (BWB) Unmanned Aerial Vehicle (UAV). The platform, designated S-3M, is an evolution of the RX-3 1:3 sub-scale demonstrator developed and flight-tested by the Laboratory of Fluid Mechanics and Turbomachinery (LFMT) during the DELAER project. The S-3M is redesigned for catapult launch and Intelligence–Surveillance–Reconnaissance (ISR) missions, supporting a useful payload of up to 5 kg. Strict dimensional, cost, and development constraints posed challenges in preserving aerodynamic efficiency and achieving sufficient stability margins. To meet these requirements, the design incorporates C-type winglets, tailored to enhance aerodynamic performance while providing stabilizing effects. Their integration enabled an increase in gross take-off weight (GTOW) and payload capacity, while ensuring adequate trimming without the need for a conventional horizontal tail. The aerodynamic development of the winglets and the overall configuration is supported by Computational Fluid Dynamics (CFD) analyses, followed by performance calculations. S-3M was manufactured by Carbon Fiber Technologies (CFT) and successfully flight-tested by LFMT, validating the design choices. Overall, the study demonstrates that C-type winglets can significantly improve efficiency and expand the operational envelope of BWB UAVs, highlighting the value of non-planar lifting surfaces in modern UAV design. Full article

UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) module in the backbone to stabilize feature activations under strong thermal clutter. Within the neck architecture, an Efficient Attentional Scale-Sequence Fusion (EASF) strategy reduces cross-scale semantic misalignment and ensures precise spatial coherence. Additionally, an EfficientViM-based state-space module captures global contextual dependencies while maintaining linear computational complexity. Finally, the Content-Guided Triple-Attention Fusion (CGTAFusion) module maximizes feature discriminability by calibrating fusion representations across the channel, spatial, and pixel dimensions. Extensive experiments on the HIT-UAV and IRSTD-1k benchmarks validate the efficacy of the DFS-YOLO framework. Compared to the baseline YOLOv12, DFS-YOLO’s performance has been significantly improved, increasing mAP@50 and mAP@50-95 by 10.16% and 7.55% on HIT-UAV, and by 1.84% and 3.18% on IRSTD-1k, respectively. These quantitative gains establish DFS-YOLO as a highly robust and state-of-the-art solution for complex infrared aerial surveillance. Full article

Object detection from unmanned aerial vehicle (UAV) imagery is essential for applications such as traffic monitoring, disaster response, and urban surveillance, yet most existing methods are developed and evaluated under clear-sky conditions. In real-world UAV operations, adverse weather including fog, rain, and snow introduces severe image degradation that simultaneously disrupts both the geometric and photometric properties of targets. This paper identifies two fundamental bottlenecks underlying this performance collapse: the lack of geometric invariance in standard convolutional operators and the inability of fixed receptive fields to reconstruct features corrupted by atmospheric interference. To address these bottlenecks, we propose SELPNet (Scene Exploration and Large Format Perception Network), a unified framework that integrates geometric alignment and multi-scale contextual perception into the YOLOv13 head. SELPNet consists of two key modules: (1) The Scene Exploration Convolution (SEC) leverages affine Lie group theory to construct a discrete manifold of rotation and scale transformations, actively probing multiple geometric views and selecting the most coherent response via a Maxout mechanism. (2) The Large Format Perception Module (LPM) introduces a dynamic dilation strategy with depthwise separable convolutions, progressively enlarging the receptive field from fine-grained edge preservation to scene-level contextual perception for semantic completion of degraded regions. We further construct and release AWU-OBB, a large-scale benchmark containing over 18,000 oriented bounding box-annotated UAV images across four representative scene categories. Ablation experiments demonstrate that SEC and LPM yield complementary gains, achieving a combined improvement of +4.26% mAP50 over the YOLOv13-n baseline with only 0.11 M additional parameters and 0.2 extra GFLOPs. The source code will be publicly released upon acceptance of this paper. Full article

This paper presents a proposed architecture for an artificial intelligence-driven unmanned aerial vehicle (UAV) system intended for tactical intelligence, surveillance, and reconnaissance (ISR) missions. The architecture brings together electro-optical imaging, long-wave infrared sensing, two-dimensional light detection and ranging (LiDAR), inertial navigation support, onboard edge computing, and resilient communication links within a unified system-level framework. Unlike many existing approaches that treat perception, autonomy, communication, and safety as loosely coupled functions, the proposed architecture combines multi-modal sensing, operator-supervised autonomy, and a safety-oriented decision validation layer intended for future integration with Ansys SCADE. The system is structured around operational and sensor-performance requirements used to justify the selection and interaction of the main onboard subsystems. At the architectural level, the proposed framework is intended to support target detection, tracking, environment awareness, and mission-level decision support under degraded visibility, constrained communication, and contested operating conditions. The paper therefore contributes a requirement-driven and safety-aware ISR UAV architecture that provides a scalable basis for future implementation, validation, and multi-UAV extension. Full article

Wildfire disasters are increasing in scale and severity, underscoring the need for more capable and coordinated aerial firefighting systems. This work presents a performance-based integrated aerial system framework that links the aircraft design tool RISPECT+ with the wildfire mission analysis tool SoSID Toolkit+ to evaluate and optimize system-level effectiveness. Incorporating terrain-specific wildfire characteristics, the framework identifies optimal aircraft configurations and deployment strategies that maximize integrated measurement of effectiveness across diverse regions. A unified surveillance platform strengthens the system of systems architecture and supports the operation of aerial firefighting aircraft. Results show enhanced system-oriented design and multi-agent coordination, with future work focused on optimal designs across diverse aircraft configurations and integrating operational environmental factors relevant to aerial firefighting. Full article

Unmanned aerial vehicle (UAV) imaging systems are increasingly deployed in surveillance, infrastructure monitoring, and smart-city applications, where the integrity of captured visual data is critical. Recent advances in generative models enable highly realistic deepfake manipulations that can compromise aerial sensor streams, particularly under real-world degradations such as motion blur, sensor noise, and compression artifacts. This paper introduces DeepLayer-ID, a degradation-aware multi-domain forensic framework specifically designed for UAV sensing environments. The proposed architecture decomposes forensic evidence into complementary spatial, frequency, and residual domains. A discrete wavelet transform module captures sub-band energy inconsistencies, while high-pass residual filtering isolates sensor pattern anomalies. A lightweight transformer-based fusion mechanism adaptively integrates cross-domain representations to enhance robustness under heterogeneous acquisition conditions. To emulate operational UAV pipelines, we construct a balanced dataset of 1096 aerial frames derived from the VisDrone2019-DET validation subset, incorporating synthetic manipulations and physics-consistent degradations. The experimental results show that DeepLayer-ID achieves 97.8% accuracy and 0.991 AUC, outperforming ResNet-50 (90.9%, 0.942 AUC), XceptionNet (92.4%, 0.957 AUC), and Noiseprint CNN (93.1%, 0.964 AUC). Notably, the model maintains real-time feasibility, with only 5.4 M parameters and 9.8 ms inference latency. These findings demonstrate that structured multi-domain signal decomposition combined with attention-guided fusion provides a robust and computationally efficient solution for deepfake detection in degraded UAV sensing systems. Full article

This paper investigates the trajectory planning problem for multiple unmanned aerial vehicles (UAVs) tasked with monitoring ground targets in complex, uneven terrains. The key challenge lies in maintaining continuous Line-of-Sight (LoS) while satisfying non-holonomic motion constraints and handling terrain-induced occlusions. To address this problem, we propose a Beam-search Model Predictive Control (BMPC) framework. The method integrates a first-order kinematic predictor for target motion estimation and a proactive safety altitude margin to guide UAVs toward favorable viewpoints before occlusions occur. The proposed approach is validated through extensive simulations based on high-resolution Digital Elevation Models (DEMs). Monte Carlo results demonstrate a significant reduction in LoS occlusion, decreasing the average occlusion rate from $38.75\pm 26.12\%$ to near zero in the noise-free case, compared with conventional reactive MPC methods. Under perception noise with a standard deviation of $1.5$ m, the LoS retention rate remains above $99\%$, indicating strong robustness to sensing uncertainty. In addition, the algorithm maintains stable computational performance, with an average execution time of approximately $1.68$ s per step in a non-optimized simulation environment. The proposed framework provides an effective solution for autonomous aerial surveillance in environments with substantial elevation variations, such as mountainous regions and urban canyons, by achieving a balance between tracking continuity and computational tractability. Full article

In sandstorm environments, a large number of suspended particles in the air absorb and scatter light, causing strong color bias, low contrast, and blurred details in images. These degradations reduce the reliability of computer vision applications in surveillance systems, intelligent transportation systems, unmanned aerial vehicle monitoring, and outdoor autonomous driving systems. A complete sandstorm image enhancement method was developed in this study by combining sky detection, color correction, contrast enhancement, and adaptive dark channel prior (ADCP) dehazing. The Lab color space was used to correct the color bias. The L channel was enhanced using normalized gamma correction and contrast-limited adaptive histogram equalization to improve brightness and contrast. Then, the sky region is detected to avoid over-processing, preserving the natural appearance of the sky region. Finally, ADCP is applied to non-sky regions for further dehazing. Experiments show that the proposed method provides better subjective and objective performance compared to other algorithms. Full article

To address the challenges of scarce unmanned aerial vehicle (UAV) track samples, severe class imbalance, and high motion similarity between UAVs and birds in low-altitude radar recognition, this paper proposes a trajectory classification method integrating a TCN-Transformer model with a physics-constrained TimeGAN (PC-TimeGAN) data augmentation framework. Specifically, the PC-TimeGAN generates high-quality, kinematically compliant UAV trajectories to alleviate data scarcity and class imbalance. A multi-scale TCN-Transformer is then constructed to comprehensively extract features, utilizing multi-kernel dilated convolutions for local temporal correlations and self-attention mechanisms for global temporal dependencies, thereby improving the discrimination between UAV and bird trajectories with similar motion patterns. Furthermore, a joint loss function combining Focal Loss and Triplet Loss is employed to optimize the decision boundaries and feature space, enhancing model robustness and generalization. Experiments on a measured dataset demonstrate that, under the 15-dimensional input setting, the proposed method achieves a UAV recall of 80.00%, an FAR of 3.15%, a precision of 64.00%, and an F1-score of 0.7111. Compared to baseline methods (e.g., SVM, LSTM, GRU, Transformer, and 1D-CNN), the proposed approach significantly improves UAV recall under limited trajectory information while keeping the false-alarm rate of misclassifying birds as UAVs low. Ultimately, this method markedly enhances the comprehensive performance of rapid track-level target classification for low-altitude surveillance radars. Full article

Unmanned Aerial Vehicles (UAVs) have become widely used tools for different applications including surveillance, search and rescue, and package delivery. However, autonomous path planning in dynamic environments with moving obstacles, wind disturbances, and energy constraints remains a significant challenge. This paper proposes a novel Multi-Head Attention Deep Q-Network with Prioritized Experience Replay (MA-DQN + PER) that integrates bio-inspired attention mechanisms with deep reinforcement learning for efficient UAV path planning. Our approach features a 46-dimensional state space that captures all environmental information, including static obstacles, wind conditions, and energy status. The proposed Attention-QNetwork architecture uses four specialized attention heads to selectively focus on different aspects of the environment, including obstacle avoidance, target tracking and energy management, and wind compensation. To improve sample efficiency and convergence speed, we incorporate Prioritized Experience Replay (PER) as well as Prioritized Experience Replay (PER) with a sum-tree data structure to improve sample efficiency and convergence speed. A curriculum learning strategy that includes 10 difficulty levels is designed to progressively enhance the agent’s capabilities. Extensive simulations demonstrate that our MA-DQN + PER approach reaches a 96% task success rate (defined as the percentage of episodes where the UAV successfully reaches the target without collision or battery depletion), while the convergence speed was 68% quicker than that of the baseline DQN. Our method demonstrates superior performance in path efficiency (+17%), energy consumption reduction (−26%), and collision avoidance compared to state-of-the-art algorithms. Full article

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article