Search Results (219)

Ship monitoring using Synthetic Aperture Radar (SAR) data faces significant challenges in detecting small vessels due to low spatial resolution and speckle noise. While ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) has shown promise for image super-resolution, it struggles with SAR imagery characteristics. This study proposes SA/SU-ESRGAN, which extends the SU-ESRGAN framework by incorporating a spatial attention mechanism loss function. SU-ESRGAN introduced semantic structural loss to accurately preserve ship shapes and contours; our enhancement adds spatial attention to focus reconstruction efforts on ship regions while suppressing background noise. Experimental results demonstrate that SA/SU-ESRGAN successfully detects small vessels that remain undetectable by SU-ESRGAN, achieving improved detection capabilities with a PSNR of approximately 26 dB (SSIM is around 0.5) and enhanced visual clarity in ship boundaries. The spatial attention mechanism effectively reduces noise influence, producing clearer super-resolution results suitable for maritime surveillance applications. Based on the HRSID dataset, a representative dataset for evaluating ship detection performance using SAR data, we evaluated ship detection performance using images in which the spatial resolution of the SAR data was artificially degraded using a smoothing filter. We found that with a 4 × 4 filter, all eight ships were detected without any problems, but with an 8 × 8 filter, only three of the eight ships were detected. When super-resolution was applied to this, six ships were detected. Full article

During the COVID-19 pandemic, SARS-CoV-2 quickly spread all over the world in a pattern of waves. In Mexico, the first wave was from March 2020 to September 2020, and during this time autopsies were forbidden. After that, the postmortem lung samples allowed us to identify histological alterations because of COVID-19. Moreover, SARS-CoV-2 infections are characterized by the manifestation of cytopathic effects like inclusion bodies, and multinucleated cells in alveolar spaces and alveolar walls. Additionally, atypical, enlarged cells, presence of macrophages in alveolar spaces, and congestion of vascular vessels were the other histopathologic alterations of the lung. Our study covered the analysis of nine postmortem lung samples from patients with severe COVID-19 diagnosed by qRT-PCR. The samples were stained with Hematoxylin-Eosin to identify the histological alterations related to lung architecture and cell populations and were subjected to immunohistochemistry for the SARS-CoV-2 Spike and Nucleocapsid proteins. All samples showed alterations associated with diffuse alveolar damage and 1/9 presented no alveolar space, 5/9 presented different levels of pleural fibrosis, and 4/9 presented distention of the small capillaries. Immunohistochemistry results revealed that 4/9 samples showed Spike-positive cytoplasmic inclusion bodies in type I pneumocytes and 2/9 Spike-positive nuclear inclusion bodies in type I pneumocytes. These inclusion bodies were found to be eosinophilic with H&E stains. The H&E results suggest tissue alterations that may contribute to the signs and symptoms of severe COVID-19, as well as the Spike protein expression, as its distribution suggests its participation in pathophysiology. Full article

Synthetic aperture radar (SAR) is a critical enabling technology for maritime surveillance. However, maneuvering ships often appear defocused in SAR images, posing significant challenges for subsequent ship detection and recognition. To address this problem, this study proposes an improved iteration phase gradient resampling autofocus (IIPGRA) method. First, we extract the defocused ships from SAR images, followed by azimuth decompression and translational motion compensation. Subsequently, a centerline-driven adaptive azimuth partitioning strategy is proposed: the geometric centerline of the vessel is extracted from coarsely focused images using an enhanced RANSAC algorithm, and the target is partitioned into upper and lower sub-blocks along the azimuth direction to maximize the separation of rotational centers between sub-blocks, establishing a foundation for the accurate estimation of spatially variant phase errors. Next, phase gradient autofocus (PGA) is employed to estimate the phase errors of each sub-block and compute their differential. Then, resampling the original echoes based on this differential phase error linearizes non-uniform rotational motion. Furthermore, this study introduces the Rotational Uniformity Coefficient (β) as the convergence criterion. This coefficient can stably and reliably quantify the linearity of the rotational phase, thereby ensuring robust termination of the iterative process. Simulation and real airborne SAR data validate the effectiveness of the proposed algorithm. Full article

The Baltic Sea region presents challenging environmental and operational conditions for search and rescue (SAR) and oil spill recovery activities, including strong winds, high waves, seasonal ice, and low water temperatures. The current Lithuanian search and rescue and oil pollution response capabilities, particularly the existing vessel “Šakiai”, are insufficient to meet modern operational and safety requirements. This study aims to determine the optimal concept and technical characteristics of a new vessel capable of operating effectively in Lithuanian maritime responsibility area. The research combines hydrometeorological data analysis, review of international regulatory frameworks, evaluation of equipment requirements, and bridge simulator modelling of two reference vessel concepts: patrol-type and supply-type. Additional oil spill dispersion modelling was performed using the simulation tool. Findings show that search and rescue tasks prioritize speed, while spill response operations require stability and maneuverability. Simulations indicate that patrol-type vessels reach search and rescue zones faster, while supply-type vessels provide superior station maintenance and equipment deployment in adverse conditions. The optimal vessel concept should be based on a supply-type hull with dynamic positioning, ≥15 kn speed, ≥113 t bollard pull, ≥6-day endurance and oil recovery arms with ≥40 m sweep width. Full article

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations $({\sigma }_r,{\sigma }_a,{\sigma }_c)$ are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows. Full article

Maritime surveillance has become increasingly relevant due to the growth of shipping, illegal fishing, and the need to monitor remote oceanic regions. Synthetic Aperture Radar (SAR) imagery supports this task under day-and-night and almost all-weather conditions. However, automatic ship detection in heterogeneous ocean environments still faces challenges, especially regarding computational cost. This study develops and compares approaches for detecting vessels in SAR imagery using radar backscatter statistics (${\sigma }^0$) to identify and characterize maritime targets. The OpenSARShip 2.0 dataset, which provides ship samples with AIS-based validation and reliable ${\sigma }^0$ estimates by type and size, was combined with maritime physical parameters such as wave age (from ERA5 reanalysis). The objective is to combine fast processing, robustness to sea variability, and inference capability regarding target size for operational applications. Four algorithms were evaluated: Rapid Thresholding (RT), based on OpenSARShip ${\sigma }^0$ values by ship length; Adjusted Rapid Thresholding (ART), with clutter-adapted thresholds; CFAR G$\Gamma$D, based on Gamma pdf modeling of ocean clutter; and a Hybrid Strategy combining RT with CFAR G$\Gamma$D. Results showed that CFAR G$\Gamma$D achieved the highest recall (87.4%) but at high computational cost, while the Hybrid Strategy (HS) offered comparable performance (Recall: 86.6%; F1-score: 74.8%) with 18× faster execution time. RT and ART were faster but less sensitive. These findings highlight the HS as an efficient compromise, supporting scalable, near-real-time vessel detection systems. Full article

Ship detection plays a pivotal role in safeguarding maritime security, regulating vessel traffic, and bolstering national maritime defense. While contemporary lightweight models predominantly emphasize parameter reduction, efforts to curtail computational demands remain underexplored. In this study, we propose a lightweight multi-feature channel convolution module (MFC-Conv) to create an efficient backbone network. This module adeptly propagates multi-scale feature information, yielding a holistic representation while approximating residual architectures in a computationally frugal manner, thereby promoting seamless gradient flow during optimization. Notably, MFC-Conv can be re-parameterized into a streamlined two-layer convolutional structure devoid of branching or partitioning, streamlining deployment on resource-constrained edge devices. Complementing this, a multi-feature attention module (MFA) is proposed to augment localization and classification efficacy with negligible overhead. Furthermore, leveraging the inherent resolution traits of satellite SAR imagery, the decoder is refined to minimize redundant computations. Empirical evaluations across diverse datasets reveal that our framework outperforms the baseline by slashing parameters by 57.8% and FLOPs by 42.7%. Relative to two leading lightweight state-of-the-art (SOTA) models, it achieves computational reductions of 51.4% and 25.0%, respectively, thereby enabling viable onboard satellite deployment for ship detection. Full article

Six years after its deployment, SatShipAI, an operational platform combining AI models with Sentinel-1 SAR imagery and AIS data, has provided robust maritime surveillance around Denmark. A meta-analysis of archived outputs, logs, and manual reviews shows stable vessel detection and classification performance over time, including successful cross-sensor application to X-band SAR data without retraining. Key operational challenges included orbit file delays, nearshore detection limits, and emerging infrastructure such as wind farms. The platform proved particularly valuable for detecting offshore “dark” vessels beyond AIS coverage, informing maritime security, traffic management, and emergency response. These findings demonstrate the feasibility, resilience, and adaptability of long-term AI–geospatial systems, offering practical guidance for future autonomous monitoring infrastructure. Full article

Small-vessel detection in Synthetic Aperture Radar (SAR) imagery constitutes a critical capability for maritime surveillance systems. However, prevailing methodologies such as sea-clutter statistical models and deep learning-based detectors face three fundamental limitations: weak target scattering signatures, complex sea clutter interference, and computational inefficiency. These challenges create inherent trade-offs between noise suppression and feature preservation while hindering high-resolution representation learning. To address these constraints, we propose the Multi-cue Efficient Maritime detector (MCEM), an anchor-free framework integrating three synergistic components: a Feature Extraction Module (FEM) with scale-adaptive convolutions for enhanced signature representation; a Feature Fusion Module (F²M) decoupling target-background ambiguities; and a Detection Head Module (DHM) optimizing accuracy-efficiency balance. Comprehensive evaluations demonstrate MCEM’s state-of-the-art performance: achieving 45.1% $A{P}_S$ on HRSID (+2.3pp over YOLOv8) and 77.7% $A{P}_L$ on SSDD (+13.9pp over same baseline), the world’s most challenging high-clutter SAR datasets. The framework enables robust maritime surveillance in complex oceanic conditions, particularly excelling in small target detection amidst high clutter. Full article

This study proposes a biomimetic optimization approach for maritime Search and Rescue (SAR) planning using a Genetic Algorithm (GA). The goal is to maximize the number of detected drifting targets by optimally deploying both official and civilian Search and Rescue Units (SRUs). The proposed method incorporates a POD-adjusted fitness function with collision-avoidance constraints and is enhanced by a greedy initialization strategy. To validate its effectiveness, we compare the GA against a baseline method (EAGD) that combines a (1 + 1)-Evolutionary Algorithm with greedy deployment, across 24 experiments involving 2 realistic maritime scenarios and 12 coverage conditions. Results show that GA consistently achieves higher average fitness and stability, particularly under stress-test settings involving only civilian vessels. The findings underscore the potential of biomimetic algorithms for real-time, flexible, and scalable SAR planning, while highlighting the value of civilian participation in emergency maritime operations. Full article

The circulatory and immune systems function in close coordination to maintain homeostasis and act as a frontline defense against infections. However, under certain conditions, this interaction becomes dysregulated, leading to thromboinflammation, a pathological process marked by the concurrent and excessive activation of coagulation, inflammation, and endothelial dysfunction. During viral infections, this phenomenon can markedly worsen clinical outcomes. Evidence indicates that viruses such as dengue, chikungunya, influenza, and SARS-CoV can trigger thromboinflammatory responses involving platelet activation, the release of procoagulant and pro-inflammatory mediators, and the formation of thrombi within blood vessels. While this response may initially help contain viral dissemination, in cases of high viremia it can progress to disseminated intravascular coagulation (DIC), hemorrhage, and multiple organ failure. This review compiles current evidence on thromboinflammatory mechanisms induced by arboviral and respiratory viruses and examines how these processes contribute to diseases’ pathogenesis and clinical severity. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

Despite the current level of public immunity to SARS-CoV-2, the early inflammatory events associated with respiratory distress in COVID-19 patients are not fully elucidated. Syrian golden hamsters, facultative hibernators, recapitulate the phenotype of SARS-CoV-2-induced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—induced severe acute lung injury seen in patients. In this study, we describe the predominance of the innate immune response in hamsters inoculated with four different SARS-CoV-2 variants, underscoring phenotypic differences among them. Severe inflammatory lung injury was chronologically associated with acute and significant weight loss, mainly in animals inoculated with A.2 and Delta variants. Omicron-infected animals had lower overall histopathology scores compared to other variants. We highlight the central role of endothelial injury and activation in the pathogenesis of experimental SARS-CoV-2 infection in hamsters, characterised by the presence of proliferative type I and type II pneumocytes with abundant surfactant expression, thereby maintaining hyperinflated alveolar fields. Additionally, there was evidence of intrapulmonary lymphatic vessel proliferation, which was accompanied by a lack of detectable microthrombosis in the lung parenchyma. However, white microthrombi were observed in lymphatic vessels. Our findings suggest that the physiological compensatory mechanisms that maintain respiratory homeostasis in Golden Syrian hamsters prevent severe respiratory distress and death after SARS-CoV-2 infection. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article

Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper proposes an improved multi-feature association method that integrates satellite remote sensing and AIS data, with a focus on oriented bounding box course estimation, to improve the detection of dark ships and enhance maritime surveillance. Firstly, the oriented bounding box object detection model (YOLOv11n-OBB) is trained to break through the limitations of horizontal bounding box orientation representation. Secondly, by integrating position, dimensions (length and width), and course characteristics, we devise a joint cost function to evaluate the combined significance of multiple features. Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. Finally, by integrating data from Gaofen-6 (optical) and Gaofen-3B (SAR) satellites, a day-and-night collaborative monitoring framework is constructed to address the blind spots of single-sensor monitoring during night-time or adverse weather conditions. Our results indicate that the detection model demonstrates a high average precision (AP50) of 0.986 on the optical dataset and 0.903 on the SAR dataset. The association accuracy of the multi-feature association algorithm is 91.74% in optical image and AIS data matching, and 91.33% in SAR image and AIS data matching. The association rate reaches 96.03% (optical) and 74.24% (SAR), respectively. This study provides an efficient technical tool for maritime safety regulation through multi-source data fusion and algorithm innovation. Full article