Search Results (75)

To address the significant degradation of image visibility and contrast in turbid media, this paper proposes an enhanced image dehazing algorithm. Unlike traditional polarimetric dehazing methods that exclusively attribute polarization information to airlight, our approach integrates object radiance polarization and airlight polarization for haze removal. First, sky regions are localized through multi-scale fusion of polarization and intensity segmentation maps. Second, region-specific transmittance estimation is performed by differentiating haze-occluded regions from haze-free regions. Finally, target radiance is solved using boundary constraints derived from non-haze regions. Compared with other dehazing algorithms, the method proposed in this paper demonstrates greater adaptability across diverse scenarios. It achieves higher-quality restoration of targets with results that more closely resemble natural appearances, avoiding noticeable distortion. Not only does it deliver excellent dehazing performance for land fog scenes, but it also effectively handles maritime fog environments. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

With the rapid advancement of maritime sports, the integration of auxiliary unmanned surface vehicles (USVs) has emerged as a promising solution to enhance the efficiency and safety of maritime education, particularly in tasks such as buoy deployment and escort operations. This paper presents a novel high-precision trajectory tracking control algorithm designed to ensure stable navigation of the USVs along predefined competition boundaries, thereby facilitating the reliable execution of buoy placement and escort missions. First, the paper proposes an improved adaptive fractional-order nonsingular fast terminal sliding mode control (AFONFTSMC) algorithm to achieve precise trajectory tracking of the reference path. To address the challenges posed by unknown environmental disturbances and unmodeled dynamics in marine environments, a nonlinear lumped disturbance observer (NLDO) with exponential convergence properties is proposed, ensuring robust and continuous navigation performance. Additionally, an artificial potential field (APF) method is integrated to dynamically mitigate collision risks from both static and dynamic obstacles during trajectory tracking. The efficacy and practical applicability of the proposed control framework are rigorously validated through comprehensive numerical simulations. Experimental results demonstrate that the developed algorithm achieves superior trajectory tracking accuracy under complex sea conditions, thereby offering a reliable and efficient solution for maritime sports education and related applications. Full article

Glacier classification offers a structured framework for assessing glacier characteristics and understanding their responses to climate change. In this study, we apply the Shi–Xie glacier classification system, proposed by Chinese glaciologists Shi and Xie, to evaluate the transformation of extremely continental, subcontinental, and maritime glaciers across China over the past four decades. Our results show a widespread rise in equilibrium line altitudes (ELAs), alongside complex changes in climatic and glaciological parameters. Notably, despite ongoing warming trends, nearly half of the glaciers experienced cooling at the ELA, and over two-thirds showed a decline in summer mean temperatures. This apparent contradiction is explained by elevation-induced cooling; as ELAs rise to higher altitudes, the corresponding summer air temperatures decline due to the lapse rate effect. Near-surface ice temperatures (20 m depth) were strongly consistent with changes in annual air temperature. Precipitation trends were spatially heterogeneous, yet around 70% of glaciers experienced stable or slightly increasing annual precipitation. In contrast, maritime glaciers, particularly those in the southeastern glacierized regions, exhibited marked decreases. Glacier surface velocities generally declined, with 90% of glaciers flowing at speeds below 50 m a⁻¹. Threshold-based analysis reveals that glaciers in transitional zones frequently exhibit multi-indicator deviations. Extremely continental glaciers near classification boundaries showed a shift toward warmer, wetter subcontinental conditions, while maritime glaciers tended toward drier, colder subcontinental characteristics. These findings offer new insights into the differentiated responses and ongoing transformation of glacier types in China under climate change. Full article

The number of Fritillaria species native to the Alps has long been debated, and observational biases due to the short flowering periods and the scattered distributions of endemic Fritillaria populations along the mountain range have probably made the task of botanists more complicated. Moreover, previous phylogenetic studies in Fritillaria have considered alpine taxa only marginally. To test species boundaries within the F. tubaeformis species complex and to study their phylogenetic relationships, intra- and inter-specific genetic variability of sixteen samples belonging to four Fritillaria species was carried out in different localities of the Maritime and Ligurian Alps, with extensions to the rest of the Alpine arc. The combined use of five plastid DNA markers (matK, ndhF, rpl16, rpoC1, and petA-psbJ) and nrITS showed that F. tubaeformis and F. burnatii are phylogenetically independent taxa, fully confirming morphological and morphometric divergences and, that F. burnatii is not related phylogenetically to the central European F. meleagris. Our phylogenetic study also supports the separation of F. tubaeformis from F. moggridgei, pointing to environment/ecological constraints or reproductive barriers as possible causes of their distinct evolutionary status. Our analysis also showed that the mountain endemic F. involucrata is not closely related to F. tubaeformis, contrasting with previous studies. The phylogenetic analysis of the nrITS region supports a close relationship between F. burnatii and F. moggridgei, but with low statistical support. Full article

The Marine Atmospheric Boundary Layer (MABL), as a critical component of Earth’s climate system, governs the exchange of matter and energy between the ocean surface and the lower atmosphere. This study presents shipborne Doppler lidar observations conducted during 12 January to 3 February 2024, along the southeastern Chinese coast. Employing a Coherent Doppler Wind Lidar (CDWL) system onboard the R/V “Yuezhanyu” research vessel, we investigated the spatiotemporal variability of MABL characteristics through integration with ERA5 reanalysis data. The key findings reveal a significant positive correlation between MABL height and surface sensible heat flux in winter, underscoring the dominant role of sensible heat flux in boundary layer development. Through the Empirical Orthogonal Function (EOF) analysis of the ERA5 regional boundary layer height, sensible heat flux, and sea level pressure, we demonstrate MABL height over the coastal seas typically exceeds the corresponding terrestrial atmospheric boundary layer height and exhibits weak diurnal variation. The CDWL observations highlight complex wind field dynamics influenced by synoptic conditions and maritime zones. Compared to onshore regions, the MABL over offshore areas further away from land has lower wind shear changes and a more uniform wind field. Notably, the terrain of Taiwan, China, induces significant low-level jet formations within the MABL. Low-level jets and low boundary layer height promote the pollution episode observed by CDWL. This research provides new insights into MABL dynamics over East Asian marginal seas, with implications for improving boundary layer parameterization in regional climate models and advancing our understanding of coastal meteorological processes. Full article

Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime industries. Owing to their rapid spread and often unpredictable occurrence, timely and accurate detection is essential for effective containment and mitigation. An efficient detection system can significantly enhance the responsiveness of emergency teams, enabling targeted interventions that minimize ecological damage and economic loss. This paper proposes a marine radar-based oil spill detection method that combines the Significance-to-Boundary Ratio (SBR) feature with an improved Sauvola adaptive thresholding algorithm. The raw radar data was firstly preprocessed through mean and median filtering, grayscale correction, and contrast enhancement. SBR features were then employed to extract coarse oil spill regions, which were further refined using an improved Sauvola thresholding algorithm followed by a denoising step to obtain fine-grained segmentation. Comparative experiments using different threshold values demonstrate that the proposed method achieves superior segmentation performance by better preserving oil spill boundaries and reducing background noise. Overall, the approach provides a robust and efficient solution for marine oil spill detection and monitoring. Full article

Oriented ship detection in Synthetic Aperture Radar (SAR) images has broad applications in maritime surveillance and other fields. While deep learning advancements have significantly improved ship detection performance, persistent challenges remain for existing methods. These include the inherent misalignment between regression and classification tasks and the boundary discontinuity problem in oriented object detection. These issues hinder efficient and accurate ship detection in complex scenarios. To address these challenges, we propose TIAR-SAR, a novel oriented SAR ship detector featuring a task interaction head and composite angle regression. First, we propose a task interaction detection head (Tihead) capable of predicting both oriented bounding boxes (OBBs) and horizontal bounding boxes (HBBs) simultaneously. Within the Tihead, a “decompose-then-interact” structure is designed. This structure not only mitigates feature misalignment but also promotes feature interaction between regression and classification tasks, thereby enhancing prediction consistency. Second, we propose a joint angle refinement mechanism (JARM). The JARM addresses the non-differentiability problem of the traditional rotated Intersection over Union (IoU) loss through the design of a composite angle regression loss (CARL) function, which strategically combines direct and indirect angle regression methods. A boundary angle correction mechanism (BACM) is then designed to enhance angle estimation accuracy. During inference, BACM dynamically replaces an object’s OBB prediction with its corresponding HBB if the OBB exhibits excessive angle deviation when the angle of the object is near the predefined boundary. Finally, the performance and applicability of the proposed methods are evaluated through extensive experiments on multiple public datasets, including SRSDD, HRSID, and DOTAv1. Experimental results derived from the use of the SRSDD dataset demonstrate that the mAP50 of the proposed method reaches 63.91%, an improvement of 4.17% compared with baseline methods. The detector achieves 17.42 FPS on 1024 × 1024 images using an RTX 2080 Ti GPU, with a model size of only 21.92 MB. Comparative experiments with other state-of-the-art methods on the HRSID dataset demonstrate the proposed method’s superior detection performance in complex nearshore scenarios. Furthermore, when further tested on the DOTAv1 dataset, the mAP50 can reach 79.1%. Full article

To ensure safe navigation, ship operators must not only meet the criteria defined in the International Maritime Organization (IMO) maneuverability standards but also understand maneuvering characteristics in restricted waters. This study numerically analyzed the hydrodynamic lateral forces and yaw moments acting on a stern trawler, a container ship, and a very large crude carrier (VLCC) with different hull forms as they navigated near a semi-circular bank wall. The effects of varying bank radius, lateral clearance, and water depth were examined. The results showed that the VLCC experienced the strongest attractive lateral force, while the stern trawler exhibited the most significant yaw moment. The hydrodynamic interaction patterns of the stern trawler and container ship were similar, whereas the VLCC displayed distinct behavior due to its fuller hull and greater inertia. These findings demonstrate that hull geometry significantly influences hydrodynamic interactions near boundaries, and the degree of response varies by ship type. The results provide valuable reference data for improving navigation safety in confined waters and preventing marine accidents such as collisions and groundings. This study contributes to a better understanding of ship–bank interaction and offers a theoretical basis for maneuvering assessments of various ship types in restricted maritime environments. Full article

High-density cities have obvious characteristics of compact urban spatial form and intensive land use in terms of spatial environment, and have always been a topic of academic focus. As a typical coastal historical district, the Macau Peninsula pier district (mainly the Macau Inner Harbour) has a high building density and a low average street width, forming a vertical coastline development model that directly converses with the ocean. This area is adjacent to Macau’s World Heritage Site and directly related to the Marine trade functions. The distribution pattern of cultural heritage linked by the ocean has strengthened Macau’s unique positioning as a node city on the Maritime Silk Road. This text is based on the theory of urban development, integrates spatial syntax and POI analysis techniques, and combines the theories of waterfront regeneration, high-density urban form and post-industrial urbanism to integrate and deepen the theoretical framework, and conduct a systematic study on the urban spatial characteristics of the coastal area of the Macau Peninsula. This study found that (1) Catering and shopping facilities present a dual agglomeration mechanism of “tourism-driven + commercial core”, with Avenida de Almeida Ribeiro as the main axis and radiating to the Ruins of St. Paul’s and Praça de Ponte e Horta, respectively. Historical blocks and tourist hotspots clearly guide the spatial center of gravity. (2) Residential and life service facilities are highly coupled, reflecting the spatial logic of “work-residence integration-service coordination”. The distribution of life service facilities basically overlaps with the high-density residential area, forming an obvious “living circle + community unit” structure with clear spatial boundaries. (3) Commercial and transportation facilities form a “functional axis belt” organizational structure along the main road, with the Rua das Lorchas—Rua do Almirante Sérgio axis as the skeleton, constructing a “functional transmission chain”. (4) The spatial system of the Macau Peninsula pier district has transformed from a single center to a multi-node, network-linked structure. Its internal spatial differentiation is not only constrained by traditional land use functions but is also driven by complex factors such as tourism economy, residential migration, historical protection, and infrastructure accessibility. (5) Through the analysis of space syntax, it is found that the core integration of the Macau Peninsula pier district is concentrated near Pier 16 and the northern area. The two main roads have good accessibility for motor vehicle travel, and the northern area of the Macau Peninsula pier district has good accessibility for long and short-distance walking. Full article

Inverse synthetic aperture radar (ISAR) ship target detection is of great significance and has broad application prospects in scenarios such as protecting marine resources and maintaining maritime order. Existing ship target detection techniques, especially target detection methods and detection models in complex settings, have problems such as long inference time and unstable robustness, meaning that they can easily miss the best time for detecting ship targets and cause intelligence loss. To solve these problems, this study proposes a new ISAR target detection model for ships based on deep learning—Complex ISAR Detection Net (CIDNet). The model is based on the Boundary Box Efficient Transformer (BETR) architecture, which combines super-resolution preprocessing, a deep feature extraction network, a feature fusion technique, and a coordinate maintenance mechanism to improve the detection accuracy and real-time performance of ship targets in complex settings. The CIDNet improves the resolution of the input image via the super-resolution preprocessing technique, which enhances the rendering of details of ship targets in the image. The feature extraction part of the model combines the efficient feature extraction capability of YOLOv10 with the global attention mechanism of BETR. It efficiently combines information from different scales and levels through a feature fusion strategy. In addition, the model integrates a coordinated attention mechanism to enhance the focus on the target region and optimize the detection accuracy. The experimental results show that CIDNet exhibits stable performance on the test dataset. Compared with existing models such as YOLOv10 and Faster R-CNN, CIDNet improves precision, recall, and the F1 score, especially when dealing with smaller targets and complex background conditions. In addition, CIDNet achieves a detection frame rate of 63, demonstrating its fine real-time processing capabilities. Full article

The digital transformation of the maritime sector, driven by Industry 4.0, is reshaping port operations toward smarter and more sustainable models. This paper analyzed the implementation of Port 4.0 technologies in the Spanish port system through the lens of the Blue Ocean Strategy. By redefining competitive boundaries and applying tools such as the Four Actions Framework and value innovation curves, the study proposes a new strategic vision where ports collaborate rather than compete. Key enabling technologies (such as Big Data, IoT, AI, and Blockchain) were assessed for their capacity to optimize energy use, reduce emissions, and enhance operational efficiency. The findings highlight the potential for a unified, data-driven port ecosystem that creates a new uncontested market space for Spanish ports while promoting environmental and economic sustainability. Full article

The 6061 aluminum alloy is extensively utilized in the production of aircraft components, valve parts, and maritime equipment, owing to its exceptional corrosion resistance, weldability, machinability, and anodic oxidation performance. This study investigates the effects of different heat treatment parameters on the mechanical properties of 6061 aluminum alloy. A series of orthogonal experiments were conducted, including quasi-static tensile tests using a QJBV212F-300KN universal testing machine following different solution and aging treatments. Scanning electron microscopy (SEM) was employed for microstructural characterization, revealing the mechanisms by which different heat treatment conditions impact the alloy’s mechanical properties. The test results indicate that the plasticity of 6061 aluminum alloy improves progressively within the temperature range of 510 °C to 540 °C. However, when the solution treatment temperature is elevated to 570 °C, significant grain coarsening occurs, leading to increased brittleness at the grain boundaries and reduced plasticity. Additionally, the elongation of 6061 aluminum alloy initially decreases and then increases as the aging time increases. Based on the experiments, a Hansel–Spittel constitutive model was developed, incorporating temperature, strain rate, and strain effects to accurately predict the flow stress of 6061 aluminum alloy under varying heat treatment conditions. Full article

Amidst the rapid global urbanization and economic integration, coastal cities have undergone significant changes in urban spatial patterns. These changes have further worsened the complex urban thermal environment, making it crucial to study the interaction between human-driven development and natural climate systems. To address the insufficient quantification of marine elements in the urban planning of subtropical coastal zones, this study takes Xiamen, a typical deep-water port city, as an example to construct a spatial analysis framework integrating marine boundary layer parameters. This research employs interpolation simulation, atmospheric correction, and other techniques to simulate the inversion of land use and Landsat 8 data, deriving urban morphological elements and Land Surface Temperature (LST) data. These data were then assigned to 500 m grids for analysis. A bivariate spatial auto-correlation model was applied to examine the relationship between urban carbon emission and LST. The study area was categorized based on the influence of marine factors, and the spatial relationships between urban morphological elements and LST were analyzed using a multiscale geographically weighted regression model. Three Xiamen-specific discoveries emerged: (1) the marine exerts a significant thermal mitigation effect on the city, with an average influence range of 7.94 km; (2) the relationship between urban morphology and the thermal environment exhibits notable spatial heterogeneity across different regions; and (3) to mitigate urban thermal environments, connected green corridors should be established in the southern coastal areas of outer districts in regions significantly influenced by the ocean. In areas with less marine influence, spatial complexity should be introduced by disrupting relatively intact blue–green spaces, while regions unaffected by the ocean should focus on increasing green spaces and reducing impervious surfaces and water bodies. These findings directly inform Xiamen’s 2035 Master Plan for combating heat island effects in coastal special economic zones, providing transferable metrics for similar maritime cities. Full article

Oceanic fronts delineate the boundaries between distinct water masses within the ocean, typically marked by shifts in weather patterns and the generation of oceanic circulation. These fronts are identified in research on intelligent oceanic front detection primarily by their significant temperature gradients. The refined identification of oceanic fronts is of great significance to maritime material transportation and ecological environment protection. In view of the weak edge nature of oceanic fronts and the misdetection or missed detection of oceanic fronts by some deep learning methods, this paper proposes an oceanic front detection method based on the U-Net model that integrates Edge-Attention-Module and the Feature Pyramid Network Module (FPN-Module). We conduct detailed statistical analysis and change rate calculation of the oceanic front, and batch process to obtain preliminary high-quality annotation data, which improves efficiency and saves time. Then, we perform manual corrections to correct missed detections or false detections to ensure the accuracy of annotations. Approximately 4800 days of daily average sea temperature fusion data from CMEMS (Copernicus Marine Environment Monitoring Service) are used for analysis, and an Encoder-Edge-FPN-Decoder Network (EEFD-Net) structure is established to enhance the model’s accuracy in detecting the edges of oceanic fronts. Experimental results demonstrate that the improved model’s front identification capability is in strong agreement with fronts segmented and annotated using the threshold method, with IoU and weighted Dice scores reaching 98.81% and 95.56%, respectively. The model can accurately locate the position of oceanic fronts, with superior detection of weak fronts compared to other network models, capturing smaller fronts more precisely and exhibiting stronger connectivity. Full article