Search Results (21)

Automatic Identification System (AIS) signals are critical for maritime traffic monitoring and collision avoidance. In satellite reception environments, signal collisions occur frequently due to large coverage areas and high ship density, severely degrading decoding performance. We propose a dual-branch deep learning architecture that combines precise boundary detection with segment-level classification to address this collision problem. The network employs a multi-scale convolutional backbone that feeds two specialized branches: one detects collision boundaries with sample-level precision, while the other provides semantic context through segment classification. We developed a satellite AIS dataset generation framework that simulates realistic collision scenarios including multiple ships, Doppler effects, and channel impairments. The trained model achieves 96% collision detection accuracy on simulated data. Validation on real satellite recordings demonstrates that our method retains 99.4% of valid position reports compared to direct decoding of the original signal. Controlled experiments show that intelligent collision removal outperforms random segment exclusion by 6.4 percentage points, confirming the effectiveness of our approach. Full article

Intelligent recognition of ship motion states is a key technology for achieving smart maritime supervision and optimized port scheduling. To enhance both the modeling efficiency and recognition accuracy of AIS trajectory data, this paper proposes a ship behavior recognition method that integrates trajectory-to-image conversion with a convolutional neural network (CNN) for classifying three typical motion states: mooring, anchoring, and sailing. Firstly, a multi-step preprocessing pipeline is established, incorporating trajectory cleaning, interpolation complementation, and segmentation to ensure data completeness and consistency; secondly, dynamic features—including speed, heading, and temporal progression—are encoded into an RGB three-channel image, which not only preserves the original spatial and temporal information of the trajectory but also strengthens the dimension of the feature expression of the image. Thirdly, the lightweight CNN architecture (CNN-Lite) is designed to automatically extract spatial motion patterns from these images, with data augmentation techniques further enhancing model robustness and generalization across diverse scenarios. Finally, comprehensive comparative experiments are conducted to evaluate the proposed method. On a real-world AIS dataset, the proposed method achieves an accuracy of 91.54%, precision of 91.51%, recall of 91.54%, and F1-score of 91.52%—demonstrating superior or highly competitive performance compared with SVM, KNN, MLSTM, ResNet-50 and Swin-Transformer in both classification accuracy and model stability. These results confirm that constructing dynamic-feature-enriched RGB trajectory images and designing a lightweight CNN can effectively improve ship behavior recognition performance and provide a practical and efficient technical solution for abnormal anchoring detection, maritime traffic monitoring, and development of intelligent shipping systems. Full article

Accurate vessel track association is a key requirement for maritime traffic monitoring and collision-avoidance systems, yet the Automatic Identification System (AIS) records commonly contain noise, missing intervals, and overlapping trajectories in congested coastal waters. We propose a Dilated Residual Connection Temporal Convolutional Network (DRC-TCN) tailored to AIS sequences; residual dilated blocks with layer normalization enable stable training while capturing long-range temporal dependencies under imperfect data. Beyond kinematic inputs, we augment AIS with buoy-based meteorological variables (wind direction and speed, gust, pressure, air temperature, and sea surface temperature) via time-aligned nearest-station fusion, allowing the model to account for environmental effects on vessel motion. Experiments on New York coastal AIS data show that DRC-TCN outperforms CNN-LSTM and vanilla TCN baselines, improving F1 score by up to 99.3% and achieving 99.7% accuracy. The results indicate that environment-aware temporal modeling strengthens the robustness of track association and supports situational awareness for next-generation intelligent navigation and ocean engineering applications. Full article

Maritime safety, environmental protection, and efficient traffic management increasingly rely on data-driven technologies. However, leveraging Automatic Identification System (AIS) data for predictive modelling faces two major challenges: the massive volume of data generated in real-time and growing privacy concerns associated with proprietary vessel information. This paper proposes a novel, privacy-preserving framework for vessel traffic density (VTD) prediction that addresses both challenges. The approach combines the European Maritime Observation and Data Network’s (EMODNet) grid-based VTD calculation method with Convolutional Neural Networks (CNN) to model spatiotemporal traffic patterns and employs Federated Learning to collaboratively build a global predictive model without the need for explicit sharing of proprietary AIS data. Three geographically diverse AIS datasets were harmonized, processed, and used to train local CNN models on hourly VTD matrices. These models were then aggregated via a Federated Learning framework under a lifelong learning scenario. Evaluation using Sparse Mean Squared Error shows that the federated global model achieves promising accuracy in sparse data scenarios and maintains performance parity when compared with local CNN-based models, all while preserving data privacy and minimizing hardware performance needs and data communication overheads. The results highlight the approach’s effectiveness and scalability for real-world maritime applications in traffic forecasting, safety, and operational planning. Full article

With the rapid development of global maritime trade, high-precision ship heading estimation has become crucial for maritime traffic safety and intelligent shipping. To address the challenge of heading estimation from horizontal-view optical images, this study proposes a novel framework integrating DeepLabV3+ image segmentation with contrastive-learning-optimized multi-scale similarity matching. First, a cascaded image preprocessing method is developed, incorporating linear transformation, bilateral filtering, and the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm to mitigate noise and haze interference and enhance image quality with improved target edge clarity. Subsequently, the DeepLabV3+ network is employed for the precise segmentation of ship targets, generating binarized contour maps for subsequent heading analysis. Based on actual ship dimensional parameters, 3D models are constructed and multi-angle rendered to establish a heading template library. The framework introduces the Multi-Scale Structural Similarity (MS-SSIM) algorithm enhanced by a triplet contrastive learning mechanism that dynamically optimizes feature weights across scales, thereby improving robustness against image degradation and partial occlusion. Experimental results demonstrate that under noise-free, noise-interfered, and mist-occluded conditions, the proposed method achieves mean heading estimation errors of 0.41°, 0.65°, and 0.88°, respectively, significantly outperforming the single-scale SSIM and fixed-weight MS-SSIM approaches. This verification confirms the method’s effectiveness and robustness, offering a novel technical solution for ship heading estimation in maritime surveillance and intelligent navigation systems. Full article

Ship-speed prediction is an emerging research area in marine traffic safety and other related fields, occupying an important position with respect to these areas. At present, the effectiveness of techniques used in in time-series forecasting methods in ship-speed prediction is poor, and there are accumulated errors in long-term forecasting, which is limited in its processing of ship-speed information combined with multi-feature data input. To overcome this difficulty and further optimize the accuracy of ship-speed prediction, this research proposes a new deep learning framework to predict ship speed by combining GANs (Generative Adversarial Networks) and LSTM (Long Short-Term Memory). First, the algorithm takes an LSTM network as the generating network and uses the LSTM to mine the spatiotemporal correlation between nodes. Secondly, the complementary characteristics linked between the generative network and the discriminant network are used to eliminate the cumulative error of a single neural network in the long-term prediction process and improve the prediction accuracy of the network in ship-speed determination. To conclude, the Generator–LSTM model advanced here is used for ship-speed prediction and compared with other models, utilizing identical AIS (automatic identification system) ship-speed information in the same scene. The findings indicate that the model demonstrates high accuracy in the typical error measurement index, which means that the model can reliably better predict the ship speed. The results of the study will assist maritime traffic participants in better taking precautions to prevent collisions and improve maritime traffic safety. Full article

SAR ship detection is of great significance in marine safety, fisheries management, and maritime traffic. At present, many deep learning-based ship detection methods have improved the detection accuracy but also increased the complexity and computational cost. To address the issue, a lightweight prior feature fusion network (LPFFNet) is proposed to better improve the performance of SAR ship detection. A perception lightweight backbone network (PLBNet) is designed to reduce model complexity, and a multi-channel feature enhancement module (MFEM) is introduced to enhance the SAR ship localization capability. Moreover, a channel prior feature fusion network (CPFFNet) is designed to enhance the perception ability of ships of different sizes. Meanwhile, the residual channel focused attention module (RCFA) and the multi-kernel adaptive pooling local attention network (MKAP-LAN) are integrated to improve feature extraction capability. In addition, the enhanced ghost convolution (EGConv) is used to generate more reliable gradient information. And finally, the detection performance is improved by focusing on difficult samples through a smooth weighted focus loss function (SWF Loss). The experimental results have verified the effectiveness of the proposed model. Full article

Synthetic aperture radar (SAR) ship detection holds significant application value in maritime monitoring, marine traffic management, and safety maintenance. Despite remarkable advances in deep-learning-based detection methods, performance remains constrained by the vast size differences between ships, limited feature information of small targets, and complex environmental interference in SAR imagery. Although many studies have separately tackled small target identification and multi-scale detection in SAR imagery, integrated approaches that jointly address both challenges within a unified framework for SAR ship detection are still relatively scarce. This study presents MC-ASFF-ShipYOLO (Monte Carlo Attention—Adaptively Spatial Feature Fusion—ShipYOLO), a novel framework addressing both small target recognition and multi-scale ship detection challenges. Two key innovations distinguish our approach: (1) We introduce a Monte Carlo Attention (MCAttn) module into the backbone network that employs random sampling pooling operations to generate attention maps for feature map weighting, enhancing focus on small targets and improving their detection performance. (2) We add Adaptively Spatial Feature Fusion (ASFF) modules to the detection head that adaptively learn spatial fusion weights across feature layers and perform dynamic feature fusion, ensuring consistent ship representations across scales and mitigating feature conflicts, thereby enhancing multi-scale detection capability. Experiments are conducted on a newly constructed dataset combining HRSID and SSDD. Ablation experiment results demonstrate that, compared to the baseline, MC-ASFF-ShipYOLO achieves improvements of 1.39% in precision, 2.63% in recall, 2.28% in ${AP}_{50}$, and 3.04% in $AP$, indicating a significant enhancement in overall detection performance. Furthermore, comparative experiments show that our method outperforms mainstream models. Even under high-confidence thresholds, MC-ASFF-ShipYOLO is capable of predicting more high-quality detection boxes, offering a valuable solution for advancing SAR ship detection technology. Full article

Small target ship detection and anomaly analysis play a pivotal role in ocean remote sensing technologies, offering critical capabilities for maritime surveillance, enhancing maritime safety, and improving traffic management. However, existing methodologies in the field of detection are predominantly based on deep learning models with complex network architectures, which may fail to accurately detect smaller targets. In the classification domain, most studies focus on synthetic aperture radar (SAR) images combined with Automatic Identification System (AIS) data, but these approaches have significant limitations: first, they often overlook further analysis of anomalies arising from mismatched data; second, there is a lack of research on small target ship classification using wide-area optical remote sensing imagery. In this paper, we develop SVIADF, a multi-source information fusion framework for small vessel identification and anomaly detection. The framework consists of two main steps: detection and classification. To address challenges in the detection domain, we introduce the YOLOv8x-CA-CFAR framework. In this approach, YOLOv8x is first utilized to detect suspicious objects and generate image patches, which are then subjected to secondary analysis using CA-CFAR. Experimental results demonstrate that this method achieves improvements in Recall and F1-score by 2.9% and 1.13%, respectively, compared to using YOLOv8x alone. By integrating structural and pixel-based approaches, this method effectively mitigates the limitations of traditional deep learning techniques in small target detection, providing more practical and reliable support for real-time maritime monitoring and situational assessment. In the classification domain, this study addresses two critical challenges. First, it investigates and resolves anomalies arising from mismatched data. Second, it introduces an unsupervised domain adaptation model, Multi-CDT, for heterogeneous multi-source data. This model effectively transfers knowledge from SAR–AIS data to optical remote sensing imagery, thereby enabling the development of a small target ship classification model tailored for optical imagery. Experimental results reveal that, compared to the CDTrans method, Multi-CDT not only retains a broader range of classification categories but also improves target domain accuracy by 0.32%. The model extracts more discriminative and robust features, making it well suited for complex and dynamic real-world scenarios. This study offers a novel perspective for future research on domain adaptation and its application in maritime scenarios. Full article

Underwater Acoustic Target Recognition (UATR) is critical to maritime traffic management and ocean monitoring. However, underwater acoustic analysis is fraught with difficulties. The underwater environment is highly complex, with ambient noise, variable water conditions (such as temperature and salinity), and multi-path propagation of acoustic signals. These factors make it challenging to accurately acquire and analyze target features. Traditional UATR methods struggle with feature fusion representations and model generalization. This study introduces a novel high-dimensional feature fusion method, CM3F, grounded in signal analysis and brain-like features, and integrates it with the Boundary-Aware Hybrid Transformer Network (BAHTNet), a deep-learning architecture tailored for UATR. BAHTNet comprises CBCARM and XCAT modules, leveraging a Kan network for classification and a large-margin aware focal (LMF) loss function for predictive losses. Experimental results on real-world datasets demonstrate the model’s robust generalization capabilities, achieving 99.8% accuracy on the ShipsEar dataset and 94.57% accuracy on the Deepship dataset. These findings underscore the potential of BAHTNet to significantly improve UATR performance. Full article

Synthetic aperture radar (SAR) is an essential tool for monitoring and managing maritime traffic and ensuring safety. It is particularly valuable because it can provide surveillance in all weather conditions. Ship wake detection has attracted considerable attention in offshore management as it has potential for widespread use in ship positioning and motion parameter inversion, surpassing conventional ship detection methods. Traditional wake detection methods depend on linear feature extraction through image transformation processing techniques, which are often ineffective and time-consuming when applied to large-scale SAR data. Conversely, deep learning (DL) algorithms have been infrequently utilized in wake detection and encounter significant challenges due to the complex ocean background and the effect of the sea state. In this study, we propose a lightweight rotating target detection network designed for detecting ship wakes under various sea states. For this purpose, we initially analyzed the features of wake samples across various frequency domains. In the framework, a YOLO structure-based deep learning is implemented to achieve wake detection. Our network design enhances the YOLOv8’s structure by incorporating advanced techniques such as deep separation convolution and combined frequency domain–spatial feature extraction modules. These modules are used to replace the usual convolutional layer. Furthermore, it integrates an attention technique to extract diverse features. By conducting experiments on the OpenSARWake dataset, our network exhibited outstanding performance, achieving a wake detection accuracy of 66.3% while maintaining a compact model size of 51.5 MB and time of 14 ms. This model size is notably less than the existing techniques employed for rotating target detection and wake detection. Additionally, the algorithm exhibits excellent generalization ability across different sea states, addressing to a certain extent the challenge of wake detection being easily influenced by varying sea states. Full article

Utilizing time-series data from ship trajectories to forecast their subsequent movement is crucial for enhancing the safety within maritime traffic environments. The application of deep learning techniques, leveraging Automatic Identification System (AIS) data, has emerged as a pivotal area in maritime traffic studies. Within this domain, the precise forecasting of ship trajectories stands as a central challenge. In this study, we propose the multi-gated attention encoder decoder (MGAED) network, a model based on an encoder–decoder structure specialized for predicting ship trajectories in canals. The model employs a long short-term memory network (LSTM) as an encoder, combined with multiple Gated Recurrent Units (GRUs) and an attention mechanism for the decoder. Long-term dependencies in time-series data are captured through GRUs, while the attention mechanism is used to strengthen the model’s ability to capture key information, and a soft threshold residual structure is introduced to handle sparse features, thus enhancing the model’s generalization ability and robustness. The efficacy of our model is substantiated by an extensive evaluation against current deep learning benchmarks. Through comprehensive comparison experiments with existing deep learning methods, our model shows significant improvements in prediction accuracy, with an at least 9.63% reduction in the mean error (MAE) and an at least 20.0% reduction in the mean square error (MSE), providing a new solution to improve the accuracy and efficiency of ship trajectory prediction. Full article

The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising all expected traffic in a Region of Interest. We propose a model for the detection of abnormal maritime behaviors that provides the closest behaviors as context to the predictions. The normalcy model relies on two-step clustering, which is first computed based on the positions of the vessels and then refined based on their kinematics. We design for each step a similarity measure, which combined are able to distinguish boats cruising shipping lanes in different directions, but also vessels with more freedom, such as pilot boats. Our proposed abnormality detection model achieved, on a large annotated dataset extracted from AIS logs that we publish, an ROC-AUC of 0.79, which is on a par with State-of-the-Art deep neural networks, while being more computationally efficient and more interpretable, thanks to the contextualization offered by our two-step clustering. Full article

In recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-shore wireless access infrastructure. However, such an infrastructure is a heterogeneous one, managed by many independent operators and utilizing a number of different communication technologies. If a moving sea vessel is to maintain a reliable communication within such a system, it needs to employ a set of network mechanisms dedicated for this purpose. In this paper, we provide a short overview of such requirements and overall characteristics of maritime communication, but our main focus is on the link selection procedure—an element of critical importance for the process of changing the device/system which the mobile vessel uses to retain communication with on-shore networks. The paper presents the concept of employing deep neural networks for the purpose of link selection. The proposed methods have been verified using propagation models dedicated to realistically represent the environment of maritime communications and compared to a number of currently popular solutions. The results of evaluation indicate a significant gain in both accuracy of predictions and reduction of the amount of test traffic which needs to be generated for measurements. Full article

The paper’s main aim is to present the impact on the city’s road traffic generated by the Port of Gdynia’s operations and propose the optimal solution for transport network development around the port. Firstly, the authors demonstrate a case study determining the impact of heavy goods vehicles (HGVs) generated by port facilities on local traffic. To this end, the average travel time of cars in the network on selected measurement sections is conditioned on the varying number of HGVs generated by the port. Next, based on the data obtained from the traffic monitoring system, PTV Vissim software is used as a modelling tool to analyse and assess the impact on local traffic. Finally, considering the analysis’ results, the vulnerability of the transport network is discussed. The optimal solution for the transport network around the port’s area is proposed. The paper is an extended version of the materials presented at the XIX Maritime Traffic Engineering Conference. Full article