Search Results (303)

Ship detection serves as a core foundational task for marine environmental perception. However, in real marine scenarios, dense vessel traffic often causes severe target occlusion while multi-scale targets, asymmetric vessel geometries, and harsh conditions (e.g., haze, low illumination) further degrade image quality. These factors pose significant challenges to vision-based ship detection methods. To address these issues, we propose ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$, an improved framework based on YOLOv8, which integrates three key enhancements: First, a Multi-Scale Asymmetry-aware Parallelized Patch-wise Attention (MSA-PPA) module is designed in the backbone to strengthen the perception of multi-scale and geometrically asymmetric vessel targets. Second, a Deformable Convolutional Upsampling (DCNUpsample) operator is introduced in the Neck network to enable adaptive feature fusion with high computational efficiency. Third, a Wasserstein-Distance-Based Weighted Normalized CIoU (WA-CIoU) loss function is developed to alleviate gradient imbalance in small-target regression, thereby improving localization stability. Experimental results on the Unmanned Vessel Zhoushan Perception Dataset (UZPD) and the open-source Singapore Maritime Dataset (SMD) demonstrate that ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$ achieves a balanced performance between lightweight design and real-time inference, showcasing strong potential for reliable deployment on edge devices of unmanned marine platforms. Full article

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article

Ship lock zones represent bottlenecks and a particular challenge for authorities managing vessel traffic. Traditionally, the control strategy of such systems has relied heavily on the subjective judgment, experience, and tacit knowledge of ship lock operators. To address the inherent uncertainty and imprecision associated with these subjective assessments, fuzzy logic and fuzzy set theory have been adopted as appropriate mathematical frameworks. In this work, the control strategy and the Fuzzy Decision Support System (FDSS) of a single-chamber ship lock designed for two vessels on a two-way waterway are analyzed and modeled. The input data is generated based on a synthesized dataset reflecting the annual schedule of vessel arrivals. The software is based on proposals and suggestions of experienced ship lock operators, and it is further validated through vessel traffic simulations. Moreover, the development of an appropriate Supervisory Control and Data Acquisition (SCADA) system integrated with a Programmable Logic Controller (PLC) is detailed, providing the necessary infrastructure for real-time deployment of the fuzzy control algorithm. The proposed control system represents an original contribution and offers practical applications both as a decision-support tool for real-time lock management and as a training platform for novice or less experienced operators. Full article

Current state-of-the-art (SoTA) instance segmentation models often struggle to accurately segment small and densely distributed vessels. In this study, we introduce MAKSEA, a new satellite imagery dataset collected from the Makkoran Coast that contains small and overlapping vessels. We also propose an efficient and robust segmentation architecture, namely MVSegNet, to segment small and overlapping ships. MVSegNet leverages three modules on the baseline UNet++ architecture: a Multi-Scale Context Aggregation block based on Atrous Spatial Pyramid Pooling (ASPP) to detect vessels with different scales, Attention-Guided Skip Connections to focus more on ship relevant features, and a Multi-Head Self-Attention Block before the final prediction layer to model long-range spatial dependencies and refine densely packed regions. We evaluated our final model with SoTA instance segmentation architectures on two benchmark datasets including LEVIR_SHIP and DIOR_SHIP as well as our challenging MAKSEA datasets using several evaluation metrics. MVSegNet achieves the best performance in terms of F1-Score on LEVIR_SHIP (0.9028) and DIOR_SHIP (0.9607) datasets. On MAKSEA, it achieves an IoU of 0.826, improving the baseline by about 7.0%. The extensive quantitative and qualitative ablation experiments confirm that the proposed approach is effective for real-world maritime traffic monitoring applications, particularly in scenarios with dense vessel distributions. Full article

This study proposes a collision-risk assessment framework that combines an interval type-2 fuzzy inference system with Dempster–Shafer evidence theory to more reliably evaluate vessel collision risk under the uncertainty inherent in AIS-based marine navigation data. The fuzzy system models membership-function uncertainty through a footprint of uncertainty and produces time-indexed basic probability assignments that are subsequently combined through a Dempster–Shafer–based temporal integration process. Robust combination rules are incorporated to mitigate the counterintuitive results often produced by classical evidence combination. Furthermore, Lenart’s time-based criterion and Fujii’s spatial safety domain are unified to construct a three-level risk labeling scheme, overcoming the limitations of conventional binary risk classification. Case studies using real AIS data demonstrate improved predictive accuracy and significantly reduced uncertainty, particularly when using the robust symmetric combination rule. Overall, the proposed framework provides a systematic approach for handling structural uncertainty in maritime environments and supports more reliable collision-risk prediction and safer navigational decision-making. Full article

Addressing the data scarcity and complex consumption characteristics in mid-to-long-term electricity load forecasting for new canals, this study proposes a novel model based on navigation traffic volume cascade mapping. A multidimensional feature matrix integrating economic indicators, meteorological factors, and facility constraints is established, with canal similarity quantified via integrated constraint optimization weighting to derive multisource fusion weights. These enable freight volume prediction through feature migration using comprehensive transportation sharing. The “freight volume–lockage volume–electricity consumption” cascade then applies tonnage-based mapping to capture vessel evolution trends, generating lockage volume forecasts. Core consumption components are predicted through a mechanistic-data hybrid model for ship lock operations and a three-layer “Node–Behavior–Energy” framework for shore power system characterization, integrated with auxiliary consumption to produce the operational mid-to-long-term load forecast. Case analysis of the Pinglu Canal (2027–2050) reveals an overall “rapid-growth-then-stabilization” electricity consumption trend, where shore power’s proportion surges from 24.1% (2027) to 67.8% (2050)—confirming its decarbonization centrality—while lock system consumption declines from 28.6% to 17.2% reflecting efficiency gains from vessel upsizing and strict adherence to navigation intensity constraints.The model provides foundations for green canal energy deployment, proving essential for establishing eco-friendly waterborne logistics. Full article

Each maritime country produces annual reports on its maritime safety policy. The annual report details the implementation of established policies, plans, and regulations concerning the supervision and protection of rights and interests at sea. By analyzing the Annual Reports for the Republic of Croatia from 2017 to 2024, maritime traffic and activities at sea were examined. The data include the number of available inspection vessels, the nautical miles traveled, fuel consumption, and similar metrics. All this information is related to the total number of inspected vessels, which is a key performance indicator for maritime traffic control. The aim of the analysis is to determine the correlation between fuel consumption, distance traveled, number of voyages, and number of inspected vessels over eight consecutive years. Data Envelopment Analysis (DEA) is used to assess the relationship between inputs and outputs to identify which years were efficient. Additionally, the multi-criteria decision-making method PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) is used to interpret and validate the DEA results, particularly the efficiency ranking. The proposed DEA–PROMETHEE hybrid model enables decision-makers to better understand DEA results, especially when efficiency scores are very similar. In terms of practical applications, the results based on the DEA input and output analysis, extended with the PROMETHEE method, show that the optimized use of available resources contributes to increased overall maritime safety. Full article

Accurate behavioral segmentation of vessel trajectories from Automatic Identification System (AIS) is essential for maritime safety and traffic management. Existing methods often rely on predefined thresholds or emphasize geometric criteria and offer limited behavioral interpretability for mobility analysis. This paper introduces an unsupervised behavioral segmentation framework that integrates clustering with matheuristic optimization. Trajectories are cleaned with a forward sliding window, and three smoothed movement features, namely speed, acceleration, and turning rate, are computed for each point. Each feature is discretized by the Jenks Natural Breaks algorithm to extract key feature points and pointwise feature labels. Segment boundaries are near-optimally chosen from these key feature points using a Matheuristic Fixed Set Search (MFSS) that minimizes a Minimum Description Length (MDL) objective. This ensures behavioral consistency within each segment and clear separation between adjacent segments. Experiments on an AIS dataset from the Qiongzhou Strait, China, demonstrate that our proposed method yields more compact, distinctly differentiated segments than baseline methods, while preserving intra-segment behavioral continuity. These segments exhibit strong semantic coherence, making them well-suited for downstream tasks such as traffic risk assessment and route planning. Full article

Mangrove forests are biodiverse and highly productive coastal ecosystems, fundamental to fisheries and tourism. However, they are severely threatened by human activities and invasive species, particularly in port areas such as the Port of Santos, necessitating effective environmental management. This study aimed to analyze the risks of biological invasion in mangrove ecosystems stemming from port activities, with a focus on the Port of Santos (PS), Brazil. To achieve this, we conducted a bibliometric review using the Web of Science and Scopus databases, analyzed vessel traffic flows arriving at the PS over 14 years (from 2010 to 2024), and discussed alternatives to address the challenge of biological invasion. The review revealed a significant gap in the scientific literature, as few studies (9.9%, n = 71) address the intersection of maritime transport, invasive species, and mangroves in Latin American contexts. The intense and constant flow of international vessels into the Port of Santos, totaling 15,193 arrivals from more than 200 ports worldwide between 2010 and 2024, poses a persistent threat of biological invasion. This high-volume connectivity, with several foreign hubs exceeding 300 departures in the period, reinforces the role of ships as vectors transporting exotic species in ballast water and through hull fouling. This can destabilize local ecosystems and cause significant socioeconomic losses unless control measures, mediated by effective policies, regulations, and technologies, are implemented in the short term. A spatiotemporal analysis of vessel traffic flows over a 14-year period revealed persistent high-risk corridors for bioinvasion, directly linking maritime activity patterns to the threat level for adjacent mangrove ecosystems. The data indicate a substantial challenge for the PS, yet one with a high potential for resolution in the medium term, contingent upon investment in technology and regulation. Full article

The Strait of Gibraltar is a major biogeographic bottleneck connecting the Atlantic Ocean and the Mediterranean Sea, where migratory cetaceans coexist with an intense maritime traffic. To evaluate the feasibility of broadband passive acoustic monitoring (PAM) for both soundscape characterisation and cetacean detection, a short drifting-buoy experiment was conducted near Barbate, Spain, in May 2025. The system, equipped with a calibrated SoundTrap 400 recorder, continuously sampled the underwater acoustic environment for 2.5 h. Analysis of the recordings revealed vocalisations of Orcinus orca, representing the first preliminary and incomplete description of the Iberian killer whale acoustic repertoire, and numerous transient tonal events with energy peaks between 40 and 50 Hz, consistent with baleen whale sounds previously attributed to foraging fin whales (Balaenoptera physalus). Sperm whale clicks and delphinid whistles were also occasionally detected. The power spectral density analysis further showed a persistent anthropogenic component dominated by vessel noise below 200 Hz and narrow-band echosounder signals at 30 and 50 kHz. These findings confirm the potential of PAM to detect multiple cetacean species and to resolve the complex interplay between biophony and anthropophony in one of the world’s busiest marine corridors. Establishing a permanent PAM observatory in the Strait would enable continuous, non-intrusive monitoring of species presence, behaviour, and habitat use, thereby contributing to conservation efforts for endangered populations such as the Iberian killer whale. Full article

With the trend of internationalization, maritime traffic density has gradually increased. Since 2002, the International Maritime Organization (IMO) has required various types of vessels to be equipped with the Automatic Identification System (AIS). Through AIS static and dynamic data, more complete navigational information of vessels can be obtained. As the Port of Kaohsiung is currently transitioning into a smart port, this study focuses on inbound and outbound vessels of the Second Port of Kaohsiung. It considers both the safety monitoring of the smart port and environmental security, integrating a big data database to provide early warnings for abnormal navigation conditions. This study builds an integrated database based on vessel AIS data, conducts AIS big data analysis to extract useful information, and establishes a random forest model to predict whether a vessel’s course and speed during port navigation deviate from normal patterns, thereby achieving the goal of early warning. This study also helps reduce the risk of collisions caused by abnormal vessel operations and thus prevents marine pollution in the port area due to oil spills or hazardous substance leakage. Through real-time monitoring and early warning of navigation behavior, it not only enhances navigation safety but also serves as the first line of defense against marine pollution, contributing significantly to the protection of the port’s ecological environment and the promotion of sustainable development. Full article

The SS Thistlegorm, a British World War II cargo vessel sunk in 1941 in the Red Sea, is one of the world’s most visited wreck dives, attracting thousands of divers annually. This popularity has accelerated structural deterioration and artefact loss through unsustainable mooring practices, looting, and unintentional diver impacts. The Thistlegorm Project—a collaboration between Alexandria University and the University of Edinburgh—conducted high-resolution underwater photogrammetric surveys in 2017 and 2022 to create the first comprehensive baseline for monitoring change. Comparative analysis revealed both subtle and significant alterations to the wreck and its debris field, including displacement of heavy structures, artefact removal, and expanded mapping of the debris field to 21.9 ha. The study demonstrates how repeat photogrammetry enables precise documentation of deterioration, informs conservation strategies, and supports heritage management in high-traffic dive sites. The Thistlegorm serves as a model for integrating digital recording, site monitoring, and collaborative stewardship of underwater cultural heritage. Full article

Understanding past and present maritime activity patterns is critical for navigation safety, environmental assessment, and commercial operations. An increasing number of services now openly provide positioning data from the Automatic Identification System (AIS) via ground-based receivers. We show that coastal vessel activity can be reconstructed from open access data with high accuracy, even with limited data quality and incomplete receiver coverage. For three months of open AIS data in the Baltic Sea from August to October 2024, we present (i) cleansing and reconstruction methods to improve the data quality, and (ii) a journey model that converts AIS message data into vessel counts, traffic estimates, and spatially resolved vessel density at a resolution of ∼400 m. Vessel counts are provided, along with their uncertainties, for both moving and stationary activity. Vessel density maps also enable the identification of port locations, and we infer the most crowded and busiest coastal areas in the Baltic Sea. We find that on average, ≳4000 vessels simultaneously operate in the Baltic Sea, and more than 300 vessels enter or leave the area each day. Our results agree within 20% with previous studies relying on proprietary data. 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

Maritime traffic surveillance and ocean environmental protection urgently require the accurate identification of surface vessel types. Although deep learning methods have significantly improved the underwater acoustic target recognition performance, the existing models suffer from large parameter counts and fail to adapt to the multi-scale spectral features of radiated noise from different vessel types, restricting their practical deployment on power-constrained underwater sensors. To address these challenges, this paper proposes a novel path-routing convolution mechanism that achieves the discriminative extraction of cross-scale acoustic features through multi-dilation-rate parallel paths and an adaptive routing strategy and designs the MobilePR-ConvNet unified architecture that enables a single framework to automatically adapt to diverse hardware platforms through systematic width scaling. Experiments on the DeepShip and ShipsEar datasets demonstrate that the proposed method achieved 98.58% and 97.82% recognition accuracies, respectively, while maintaining a 77.8% robust performance under 10 dB low-signal-to-noise-ratio conditions, validating the cross-dataset generalization capability in complex marine environments and providing an effective solution for intelligent deployment on resource-constrained underwater devices. Full article