Maritime Monitoring Research Articles

Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data

In the domain of maritime surveillance, the continuous tracking and monitoring of vessels are imperative for the early detection of potential threats. The Automatic Identification System (AIS) database, which collects vessel movement data over time, including timestamps and other motion details, plays a crucial role in real-time maritime monitoring. However, it frequently exhibits irregular intervals of data collection and intricate, intersecting trajectories, underscoring the importance of analyzing long-term temporal patterns for effective vessel tracking. While Kalman Filters and other physics-based models have been employed to tackle these issues, their effectiveness is limited by their inability to capture long-term dependence and non-linearity in the historical data. This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. Recognizing the strengths and limitations of the LSTM model, we propose a hybrid machine-learning algorithm that integrates LSTM with a physics-based model. This combination harnesses the physical laws governing vessel movements alongside data driven pattern mining, thereby enhancing the predictive accuracy of vessel locations. To assess the performance of standalone and hybrid models, various scenarios with different levels of complexity are generated. Furthermore, to simulate real-world data loss conditions often encountered in maritime tracking, temporal data gaps are randomly introduced into the scenarios. The competing approaches are then evaluated using both with time gap and without time gap conditions. Our results show that, although the LSTM model performs better than the physics-based model, the hybrid model consistently outperforms both standalone models across all scenarios. Furthermore, while data gaps negatively impact the accuracy of all models, the performance reduction is minimal for the physics-infused model. In summary, this study not only demonstrates the potential of combining data-driven and physics-based approaches but also sets a new benchmark for maritime vessel tracking.

Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks.

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.

Remote Sensing for Maritime Monitoring and Vessel Identification

According to the statistics published by the United Nations Conference on Trade and Development [1], the total fleet worldwide consisted of more than 2 [...]

Ship Classification Based on AIS Data and Machine Learning Methods

AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus making precise identification of unfamiliar ship types crucial for maritime monitoring. This study introduces an original classification study based on AIS data for cargo ships, presenting a classifier tailored for bulk carriers, containers, general cargo, and vehicle carriers. The model’s efficacy was tested within the Changhua Wind Farm Channel using eight classification algorithms across tree-structure-based, proximity-based, and regression-based categories and employing standard metrics (Accuracy, Precision, Recall, F1-score) to assess the performance. The results show that tree-structure-based algorithms, particularly XGBoost and Random Forest, demonstrated superior performance. This study also implemented a feature selection strategy with five methods, revealing that a model trained with only four features (three ship-geometric features and one trajectory behavior feature) can achieve high accuracy. Conclusively, the classifier effectively overcame the challenges of limited AIS data labels, achieving a classification accuracy of 97% for ships in the Changhua Wind Farm Channel. These results are pivotal in identifying abnormal ship behavior, highlighting the classifier’s potential for maritime monitoring applications.

The Prospect of Using Artificial Intelligence in TNI Ship Information Systems as a Manifestation of a Resilient Maritime Defense Industry

This research investigates the prospects of using Artificial Intelligence (AI) in the Indonesian Navy's ship information system as a crucial step in developing a robust and innovative maritime defense industry. The primary focus is on the application of AI technology in the ship information system, analyzing various aspects, including the potential implementation of AI in monitoring and analyzing maritime waters, technical and security challenges that may arise, expected benefits, and the strategic impact of AI implementation in Indonesia's maritime defense industry. The research method employs the Systematic Literature Review (SLR) method, providing a systematic and comprehensive approach to collecting, evaluating, and analyzing relevant literature in a specific field. This study involves literature searches using keywords such as artificial intelligence, information systems, maritime defense industry, Indonesian defense, and Indonesian Navy ships from 2011 to 2023 in various sources such as scholarly journals, conferences, and online databases. The research findings indicate that the use of artificial intelligence in the Indonesian Navy's ship information system has significant potential to improve maritime monitoring, threat detection, operational efficiency, and international collaboration. The integration of AI with advanced sensors such as radar, sonar, and thermal cameras enables more accurate real-time monitoring of activities in maritime waters. Higher accuracy in monitoring and threat detection also contributes to increased situational awareness and smarter decision-making by operational leaders in the Indonesian Navy. Despite the substantial benefits, AI implementation faces several challenges, including initial investment costs, infrastructure availability, personnel training, data security, ethical regulations, dependence on foreign technology, integration and compatibility issues, AI technology reliability, cultural and organizational changes, and long-term benefits. To address these challenges, it is recommended that the government allocates budgets wisely, develops adequate technological infrastructure, provides personnel training, and formulates ethical regulations. Strengthening independence in the development of domestic AI technology is also crucial.

Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity

Symbolic event recognition systems detect event occurrences using first-order logic rules. Although existing online structure learning approaches ease the discovery of such rules in noisy data streams, they assume the existence of fully labelled training data. Splice is a recent online graph-based approach that estimates the labels of unlabelled data and makes it possible to learn such rules from semi-supervised training sequences of logical interpretations. However, Splice labelling depends significantly on the metric used to compute the distances of unlabelled examples to their labelled counterparts. Moreover, there is no guarantee about the quality of the labelling found in the local graphs that are built as the data stream in. In this paper, we propose a new online learning method, which includes an enhanced hybrid measure that combines an optimised structural distance, and a data-driven one. The former is guided by feature selection targeted to kNN classification, while the latter is a mass-based dissimilarity. Additionally, the enhanced Splice method, improves the graph construction process, by storing a synopsis of the past, in order to achieve more informed labelling on the local graphs. We evaluate our approach by learning Event Calculus theories for the tasks of human activity recognition, maritime monitoring, and fleet management. The evaluation suggests that our approach outperforms its predecessor, in terms of inferring the missing labels and improving the predictive accuracy of the underlying structure learning system.

Ship Detection in Navy Using Satellite Images by Deep Learning Approach

Ship identification in satellite images poses significant challenges within the domain of remote sensing. Its importance extends to critical areas such as security, encompassing concerns such as military attacks, accidents, illegal transportation of goods, illegal fishing, territorial violations, and ship hijackings. Additionally, effective traffic management and smuggling prevention heavily rely on accurate ship identification. While synthetic aperture radar (SAR) has historically dominated maritime monitoring, researchers are increasingly exploring optical satellite images as a potential alternative. Previous ship detection techniques have utilized Computer-based image processing vision methods. However, this study proposes a novel approach employing a Convolutional Neural Network (CNN) based method to accurately identify ships in satellite data. The suggested approach entails the utilization and assessment of a custom-designed deep learning model based on CNN architecture. to recognize ships in satellite photos. Keywords - ship detection, optical images, Convolutional Neural Networks.

Ship Trajectory Prediction: An Integrated Approach Using ConvLSTM-Based Sequence-to-Sequence Model

Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments.

Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images

Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.

Satellite Image Categorization Using Scalable Deep Learning

Detecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing problems. Satellite image classification has many associated challenges. These challenges include data availability, the quality of data, the quantity of data, and data distribution. These challenges make the analysis of satellite images more challenging. A convolutional neural network architecture with a scaling method is proposed for the classification of satellite images. The scaling method can evenly scale all dimensions of depth, width, and resolution using a compound coefficient. It can be used as a preliminary task in urban planning, satellite surveillance, monitoring, etc. It can also be helpful in geo-information and maritime monitoring systems. The proposed methodology is based on an end-to-end, scalable satellite image interpretation. It uses spatial information from satellite images to categorize these into four categories. The proposed method gives encouraging and promising results on a challenging dataset with a high inter-class similarity and intra-class variation. The proposed method shows 99.64% accuracy on the RSI-CB256 dataset.

A Study on Monitoring and Supervision of Ship Nitrogen-Oxide Emissions and Fuel-Sulfur-Content Compliance

Regulations for the control of air-pollutant emissions from ships within pollutant emission control areas (ECAs) have been issued for several years, but the lack of practical technologies and fundamental theory in the implementation process remains a challenge. In this study, we designed a model to calculate the nitrogen-oxide-emission intensity of ships and the sulfur content of ship fuels using theoretical deduction from the law of the conservation of mass. The reliability and availability of the derived results were empirically evaluated using measurement data for NOx, SO2, and CO2 in the exhaust gas of a demonstration ship in practice. By examining the model and the measured or registered fuel-oil-consumption rates of ships, a compliance-determination workflow for NOx-emission intensity and fuel-sulfur-content monitoring and supervision in on-voyage ships were proposed. The results showed that the ship fuel’s NOx-emission intensity and sulfur content can be evaluated by monitoring the exhaust-gas composition online and used to assist in maritime monitoring and the supervision of pollutant emissions from ships. It is recommended that uncertainties regarding sulfur content should be considered within 15% during monitoring and supervision. The established model and workflow can assist in maritime monitoring. Meanwhile, all related governments and industry-management departments are advised to actively lead the development of monitoring and supervision technology for ship-air-pollutant control in ECAs, as well as strengthening the quality management of ships’ static data.

Towards real-time detection of ships and wakes with lightweight deep learning model in Gaofen-3 SAR images

Synthetic aperture radar (SAR) is an important tool for achieving accurate and efficient monitoring of maritime traffic by identifying ships and wakes. Ship wake can not only assist in ship detection, but also invert ship navigation information, which has attracted wide attention. However, most of the work on ship wake detection is still through non-deep learning methods. Limited by factors such as unclear features, complex background interference and insufficient label information in large-scale SAR images, deep learning methods have developed slowly in the field of ship wake detection. In this paper, we propose a lightweight deep learning network for ship and wake detection. In the framework, a YOLO structure based deep learning is implemented to achieve ship and wake classification and localization. For this purpose, we initially create a Gaofen-3 SAR dataset containing about 1000 pairs of ships and wakes. To streamline the deep learning network, based on the background and pixel characteristics of SAR images, we design a combined convolutional module in combination with an attention mechanism, which replaces the convolutional layers in the backbone to extract more diverse feature maps at a low cost. In addition, in view of the geometric features of ship wake, the optimized design is conducted in the target prediction stage, and the angle-related loss function is developed to guide the training of the network, which not only ensures the detection of the ship target, but also greatly improves the accuracy of wake line detection. Experimental results show that the developed lightweight network achieves accurate and stable detection of ships and wakes in the dataset, and it has excellent generalization ability in complex background and weak target detection. The performance is comparable to the state-of-the-art models, where the average precision of ship and wake detection reaches 77.86% and 97.29%, respectively. Particularly, the average angle error between the predicted and actual values of wake lines is reduced by 3.476°. Moreover, the low energy consumption (model size is one-sixth of YOLOv4) and real-time detection (>23.1 FPS) of the algorithm offer great potential for embedded deployment.

Nighttime infrared ship target detection based on Two-channel image separation combined with saliency mapping of local grayscale dynamic range

The automatic detection of infrared ship targets at night is the key research of intelligent maritime monitoring. Because the infrared maritime small targets are always dim, owning only simple texture, and variable in shape in voyage, deep learning is not so suitable for these kinds of target detection. In this paper, two-channel image separation combined with saliency mapping of local grayscale dynamic range (TCS-SMoLGDR) algorithm for ship target detection is proposed. The saliency mapping of local grayscale dynamic range (SMoLGDR) is proposed by taking full advantage of the feature that the infrared ship target at night owns the largest grayscale dynamic range. A saliency map is generated through SMoLGDR, in which the target area is significantly enhanced and the background is effectively suppressed. On the basis of the saliency map, the connected domain mean strategy is used to determine the real target area, which is beneficial to retain the complete target. For better segmentation of the target with uneven grayscale distribution from the background, this paper proposes a two-channel image separation (TCS) method to separate the local image of the target area into a bright channel image and a dark channel image, so that the grayscale distribution of targets in sub-images becomes relatively uniform. Finally, edge-guided binarization is used to extract the target. The experiment results show fully that the algorithm proposed in this paper can achieve the effective detection of the small dim targets in the nighttime infrared maritime image, and the detection accuracy is better than the comparison algorithms.

Energy-Efficient UAV-Aided Ocean Monitoring Networks: Joint Resource Allocation and Trajectory Design

The Internet of Underwater Things (IoUT) plays a key role in maritime monitoring systems, but energy-efficient data-uploading has been a challenging task owing to energy-constrained and expensive facilities, such as buoys and underwater sensors. In this article, we present an energy-efficient data collection scheme for unmanned aerial vehicle (UAV)-aided ocean monitoring networks (OMNs), where underwater acoustic and aerial radio frequency (RF) links are considered collaboratively. Our goal is to maximize energy efficiency (EE) of the entire OMN by jointly optimizing the transmit power of buoys and sensors, scheduling their transmissions, as well as designing the UAV's trajectory; the objective function is constrained by minimum throughput thresholds, power consumption budgets, and the UAV's kinematic conditions. Furthermore, we introduce a tradeoff between the energy consumption of buoys and sensors to bridge the gap between acoustic and RF links. The formulated problem is decomposed into three subproblems and they are solved alternatively. In each iteration, we leverage Dinkelbach's method and successive convex approximation (SCA) technique to tackle the fractional program (FP) and transform an original subproblem into a convex form, respectively. Extensive simulations confirm the convergence of our proposed scheme, reveal the influence of the tradeoff on EE, and show that our scheme outweighs other benchmarks in different scenarios.

Towards a Secure and Scalable Maritime Monitoring System Using Blockchain and Low-Cost IoT Technology

Maritime Domain Awareness (MDA) is a strategic field of study that seeks to provide a coastal country with an effective monitoring of its maritime resources and its Exclusive Economic Zone (EEZ). In this scope, a Maritime Monitoring System (MMS) aims to leverage active surveillance of military and non-military activities at sea using sensing devices such as radars, optronics, automatic Identification Systems (AISs), and IoT, among others. However, deploying a nation-scale MMS imposes great challenges regarding the scalability and cybersecurity of this heterogeneous system. Aiming to address these challenges, this work explores the use of blockchain to leverage MMS cybersecurity and to ensure the integrity, authenticity, and availability of relevant navigation data. We propose a prototype built on a permissioned blockchain solution using HyperLedger Fabric—a robust, modular, and efficient open-source blockchain platform. We evaluate this solution’s performance through a practical experiment where the prototype receives sensing data from a Software-Defined-Radio (SDR)-based low-cost AIS receiver built with a Raspberry Pi. In order to reduce scalability attrition, we developed a dockerized blockchain client easily deployed on a large scale. Furthermore, we determined, through extensive experimentation, the client optimal hardware configuration, also aiming to reduce implementation and maintenance costs. The performance results provide a quantitative analysis of the blockchain technology overhead and its impact in terms of Quality of Service (QoS), demonstrating the feasibility and effectiveness of our solution in the scope of an MMS using AIS data.

Energy-based USV maritime monitoring using multi-objective evolutionary algorithms

This study addresses the monitoring mission problem using an USV equipped with an on-board LiDAR allowing to monitor regions inside its coverage radius. The problem is formulated as a bi-objective coverage path planning with two conflicting objectives : minimization of the consumed energy and maximization of the coverage rate. To solve the problem, we use two popular multi-objective evolutionary algorithms : Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Archived Evolution Strategy (PAES). First, we compare the efficiency of these two algorithms and show that PAES allows to find solutions allowing to save more energy as compared to those provided by NSGA-II. Then, we propose a new method which improves the performance of evolutionary algorithms when solving covering path planning problems by reducing the chromosome size. We have applied this method on the used algorithms and simulation results shows a significant performance enhancement both PAES and NSGA-II.

The Mathematical Model of Marine Engine Room Equipment Based on Machine Learning

This study is aimed at reducing the occurrence of oil spill accidents in the engine room of ships and carries out risk prevention for the equipment of the port ships, thereby reducing pollution to the marine ecological environment. Firstly, the concepts and principles of cluster analysis and ship automatic identification system are expounded. Secondly, the data information collected by the ship’s automatic identification system (AIS) is combined with density-based cluster analysis. The accident area and extent at different stages of the ship’s engine room equipment are classified. Finally, cluster analysis is used to evaluate the risk of equipment of the port ship engine room. The results show that there are 43000 ship operation information points in port a, the average operating speed of ships is 9 kilometers, and the fastest operation speed is 16.9 kilometers. In addition, many ship routes in port a need to take risk prevention measures to minimize the impact between ships and reduce the risk of oil leakage in the engine room. The proposed cabin model can easily and quickly analyze the orientation information of the ship and classify all the data into different types according to the surrounding information points. AIS can realize the information transfer between ships and between ship and shore. Information such as the position, speed, and direction of the ship needs to be accurately known to ensure safety at sea. These data need to relate to some terminals and networks to form a maritime monitoring network. The ship AIS based on cluster analysis can cluster the areas where the ship’s speed and direction change significantly in the port area, effectively preventing accidents. Scientific risk prevention measures can effectively reduce the oil leakage risk of ship engine room equipment, improve the working efficiency of marine engines, and provide a strong foundation for the entire marine ecological environment protection.

Ship Segmentation and Georeferencing from Static Oblique View Images.

Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22 ± 10) m for ships detected within a 400 m range, and (53 ± 24) m for ships over 400 m away from the camera.

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Synthetic aperture radar (SAR) satellites can provide microwave remote sensing images without weather and light constraints, so they are widely applied in the maritime monitoring field. Current SAR ship detection methods based on deep learning (DL) are difficult to deploy on satellites, because these methods usually have complex models and huge calculations. To solve this problem, based on the You Only Look Once version 5 (YOLOv5) algorithm, we propose a lightweight on-board SAR ship detector called Lite-YOLOv5, which (1) reduces the model volume; (2) decreases the floating-point operations (FLOPs); and (3) realizes the on-board ship detection without sacrificing accuracy. First, in order to obtain a lightweight network, we design a lightweight cross stage partial (L-CSP) module to reduce the amount of calculation and we apply network pruning for a more compact detector. Then, in order to ensure the excellent detection performance, we integrate a histogram-based pure backgrounds classification (HPBC) module, a shape distance clustering (SDC) module, a channel and spatial attention (CSA) module, and a hybrid spatial pyramid pooling (H-SPP) module to improve detection performance. To evaluate the on-board SAR ship detection ability of Lite-YOLOv5, we also transplant it to the embedded platform NVIDIA Jetson TX2. Experimental results on the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) show that Lite-YOLOv5 can realize lightweight architecture with a 2.38 M model volume (14.18% of model size of YOLOv5), on-board ship detection with a low computation cost (26.59% of FLOPs of YOLOv5), and superior detection accuracy (1.51% F1 improvement compared with YOLOv5).

Oriented Ship Detection Based on Strong Scattering Points Network in Large-Scale SAR Images

Ship detection has broad applications in many areas, including fishery management, maritime rescue, and maritime monitoring. Recently, numerous detectors based on deep learning have been carried in ship detection in synthetic aperture radar (SAR) images. However, detecting the inshore ships faces enormous challenges because of the strong scattering interference of the inland area. In order to address such issues, a novel method named strong scattering points network for ship detection is proposed in this article. First, according to the SAR imaging mechanism, the ships usually appear strong scattering phenomenon in the SAR images. Therefore, the proposed method detects the strong scattering points on the ship and then aggregates their positions to obtain the ship’s arbitrary orientation box. Second, our method designs an embedding vector to cluster these points as an individual object to regress the oriented bounding box. Third, in order to distinguish the strong scattering points on land, a ship attention module is employed to extract the image texture features and representations of local features. It can suppress the false alarm caused by land interference in the detection process. Furthermore, to demonstrate the effectiveness of the proposed algorithm, this article introduces a new ship dataset for oriented ship detection named large-scale dataset for ship detection in SAR images (LDSD). Moreover, the public SAR ship detection dataset (SSDD) is utilized to verify the robustness and generalization ability of the detector. The experimental results on two datasets show that our method has a strong anti-interference ability in the inshore background and achieves state-of-the-art detection performance.