Search Results (48)

To address the challenging problem of multi-scale inshore–offshore ship detection in synthetic aperture radar (SAR) remote sensing images, we propose a novel deep learning-based automatic ship detection method within the framework of compositional learning. The proposed method is supported by three pillars: context-guided region proposal, prototype-based model-pretraining, and multi-model ensemble learning. To reduce the false alarms induced by the discrete ground clutters, the prior knowledge of the harbour’s layout is exploited to generate land masks for terrain delimitation. To prepare the model for the diverse ship targets of different sizes and orientations it might encounter in the test environment, a novel cross-dataset model pretraining strategy is devised, where the SAR images of several key ship target prototypes from the auxiliary dataset are used to support class-incremental learning. To combine the advantages of diverse model architectures, an adaptive decision-level fusion framework is proposed, which consists of three components: a dynamic confidence threshold assignment strategy based on the sizes of targets, a weighted fusion mechanism based on president-senate check–balance, and Soft-NMS-based Dense Group Target Bounding Box Fusion (Soft-NMS-DGT-BBF). The performance enhancement brought by contextual knowledge-aided terrain delimitation, cross-dataset prototype-based model pretraining and check–balance-based adaptive decision-level fusion are validated with a series of ingeniously devised experiments based on the FAIR-CSAR-Ship dataset. Full article

The increasing complexity of maritime traffic imposes growing demands on the safety and rationality of ship-collision-avoidance decisions. While most existing research focuses on simple encounter scenarios, autonomous collision-avoidance strategies that comply with the International Regulations for Preventing Collisions at Sea (COLREGs) in complex multi-ship environments remain insufficiently investigated. To address this gap, this study proposes a novel collision-avoidance framework that integrates a quantitative COLREGs analysis with a distributed stochastic search mechanism. The framework consists of three core components: encounter identification, safety assessment, and stage classification. A cost function is employed to balance safety, COLREGs compliance, and navigational efficiency, incorporating a distance-based weighting factor to modulate the influence of each target vessel. The use of a distributed stochastic search algorithm enables decentralized decision-making through localized information sharing and probabilistic updates. Extensive simulations conducted across a variety of scenarios demonstrate that the proposed method can rapidly generate effective collision-avoidance strategies that fully comply with COLREGs. Comprehensive evaluations in terms of safety, navigational efficiency, COLREGs adherence, and real-time computational performance further validate the method’s strong adaptability and its promising potential for practical application in complex multi-ship environments. Full article

The accurate extraction of navigational regions from images of navigational waters plays a key role in ensuring on-water safety and the automation of unmanned vessels. Nonetheless, current technological methods encounter significant challenges in addressing fluctuations in water surface illumination, reflective disturbances, and surface undulations, among other disruptions, in turn making it challenging to achieve rapid and precise boundary segmentation. To cope with these challenges, in this paper, we propose a coordinate-aware multi-scale feature network (GASF-ResNet) method for water segmentation. The method integrates the attention module Global Grouping Coordinate Attention (GGCA) in the four downsampling branches of ResNet-50, thus enhancing the model’s ability to capture target features and improving the feature representation. To expand the model’s receptive field and boost its capability in extracting features of multi-scale targets, the Avoidance Spatial Pyramid Pooling (ASPP) technique is used. Combined with multi-scale feature fusion, this effectively enhances the expression of semantic information at different scales and improves the segmentation accuracy of the model in complex water environments. The experimental results show that the average pixel accuracy (mPA) and average intersection and union ratio (mIoU) of the proposed method on the self-made dataset and on the USVInaland unmanned ship dataset are 99.31% and 98.61%, and 98.55% and 99.27%, respectively, significantly better results than those obtained for the existing mainstream models. These results are helpful in overcoming the background interference caused by water surface reflection and uneven lighting in the aquatic environment and in realizing the accurate segmentation of the water area for the safe navigation of unmanned vessels, which is of great value for the stable operation of unmanned vessels in complex environments. Full article

Autonomous ship navigation systems must ensure safe and efficient route planning while complying with the International Regulations for Preventing Collisions at Sea (COLREGs). This paper presents an integrated navigation framework that combines chart-based global path planning with a Model Predictive Control (MPC) approach for local trajectory tracking and COLREG-compliant collision avoidance. The method generates feasible reference routes using maritime charts and predefined waypoints, while the MPC controller ensures precise path following and dynamic re-planning in response to nearby vessels and coastal obstacles. Coastal features and shorelines are modeled using Global Self-consistent, Hierarchical, High-resolution Geography data, enabling MPC to treat landmasses as static obstacles. Other vessels are represented as dynamic obstacles with varying speeds and headings, and COLREG rules are embedded within the MPC framework to enable rule-compliant maneuvering during encounters. To address real-time computational constraints, a simplified MPC formulation is introduced, balancing predictive accuracy with computational efficiency, making the approach suitable for embedded implementations. The navigation framework is implemented in a MATLAB-based simulation with real-time visualization supporting multi-vessel scenarios and COLREG-aware vessel interactions. Simulation results demonstrate robust performance across diverse maritime scenarios—including complex multi-ship encounters and constrained coastal navigation—while maintaining the shortest safe routes. By seamlessly integrating chart-aware path planning with COLREG-compliant, MPC-based collision avoidance, the proposed framework offers an effective, scalable, and robust solution for autonomous maritime navigation. Full article

Formulating effective typhoon avoidance routes is crucial for ensuring the safe navigation of ocean-going vessels. From a maritime safety perspective, this paper investigates ship route optimization under typhoon forecast uncertainty. Initially, the study calculates the probability of a ship encountering a typhoon based on the distribution of historical typhoon data within the radius of seven-level winds and the distance between the ship and the typhoon. Subsequently, the minimum safe distance is quantified, and a multi-objective ship route optimization model for typhoon avoidance is established. A three-dimensional multi-objective ant colony algorithm is designed to solve this model. Finally, a typhoon avoidance simulation experiment is conducted using Typhoon TAMRI and a classic route in the South China Sea as a case study. The experimental results demonstrate that under adverse conditions of uncertain typhoon forecasts, the proposed multi-objective typhoon avoidance route optimization model can effectively avoid high wind and wave areas of the typhoon while balancing and optimizing multiple navigation indicators. This model can serve as a reference for shipping companies in formulating typhoon avoidance strategies. Full article

Driven by the commercialization of intelligent ships, the increasingly complex mixed maritime traffic environment presents significant challenges for collision avoidance between multiple ships due to cognitive and behavioral differences between intelligent and traditional ships. Therefore, it is essential to develop a human-like collision avoidance strategy that incorporates traditional navigational experience and handling practices, enhancing explainability and autonomy. By addressing the actual decision-making needs for predicting other ships’ intentions and considering potential risk impacts, a hierarchical strategy is designed that first seeks course direction adjustment and then determines the magnitude of adjustment. A direction adjustment intention estimation model is proposed, accounting for risk membership and COLREGS, to predict other ships’ collision avoidance intentions. Additionally, an intention influence model and a state influence model are introduced to design decision-making objectives, forming an optimization function based on angle range and maneuvering time constraints to determine the appropriate adjustment magnitude. The results demonstrate the strategy’s effectiveness across various scenarios. Specifically, the distance between ships increased by nearly 25% during the process, significantly enhancing safety. It is worth mentioning that the model has the potential to enhance intelligent ships’ capabilities in complex situational handling and intention understanding. Full article

Inland waterway navigation involves complex traffic conditions with frequent multi-ship encounters. Benefiting from its straightforward structure and robust adaptability, reinforcement learning has found applications in navigation. This article proposes a deep actor–critic collision avoidance model which is based on the weighted summation of intrinsic reward and extrinsic reward, overcoming the sparsity of the reward function in navigation tasks. For the proposed algorithm, the extrinsic reward considers factors of collision risk, economic reward, and penalties for violating collision avoidance rules, while the intrinsic reward explores the novelty of agent states. The optimization of the own ship’s actions is achieved through the utilization of a weighted summation of these two types of rewards, providing valuable guidance for decision-making in a symmetrical interaction framework. To validate the performance of the proposed multi-ship collision avoidance model, simulations of both two-ship encounters and complex multi-ship scenarios involving dynamic and static obstacles are conducted. The following conclusions can be drawn: (1) The proposed model could provide effective decisions for ship navigation in inland waterways, maintaining symmetrical coordination between vessels. (2) The hybrid reward mechanism successfully guides ship behavior in collision avoidance scenarios. Full article

Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) representation, Label Assignment (LA) strategies, and regression loss functions. To address these limitations, this study proposes a novel detection framework, the Gaussian Detection (GaussianDet) Framework, that integrates probabilistic modeling with dynamic sample assignment to achieve more precise OD. The core design of this framework is inspired by the theory of geometric symmetry. Specifically, the radial symmetry of a two-dimensional Gaussian distribution is employed to capture the rotational and scale-invariant properties of Remote Sensing (RS) objects. By leveraging the axial symmetry of elliptical geometry, the proposed Gaussian Elliptical Intersection over Union (GEIoU) enables rotation-aligned matching, while Omni-dimensional Adaptive Assignment (ODAA) introduces dynamic symmetric constraints to optimize the spatial distribution of training samples. Specifically, a Flexible Bounding Box (FBBox) representation based on a 2D Gaussian distribution is introduced to more accurately characterize the shape, aspect ratio, and orientation of objects. In addition, the GEIoU is designed as a scale-invariant similarity metric to align regression loss with detection accuracy. To further enhance sample quality and feature learning, the ODAA strategy adaptively selects positive samples based on object scale and geometric constraints. Experimental results on the High-Resolution Ship Collection 2016 (HRSC2016) and University of Chinese Academy of Sciences–Aerial Object Detection (UCAS-AOD) datasets demonstrate that GaussianDet achieves mean Average Precision (mAP) scores of 90.53% and 96.24%, respectively. These results significantly outperform existing Oriented Object Detection (OOD) methods, thereby validating the effectiveness of the proposed approach and providing a solid theoretical foundation for future research in Remote Sensing Object Detection (RSOD). Full article

Spherical buoys serve as water surface markers, and their location information can help unmanned surface vessels (USVs) identify navigation channel boundaries, avoid dangerous areas, and improve navigation accuracy. However, due to the presence of disturbances such as reflections, water obstruction, and changes in illumination for spherical buoys on the water surface, using binocular vision for positioning encounters difficulties in matching. To address this, this paper proposes a monocular vision-based localization method for spherical buoys using elliptical fitting. First, the edges of the spherical buoy are extracted through image preprocessing. Then, to address the issue of pseudo-edge points introduced by reflections that reduce the accuracy of elliptical fitting, a multi-step method for eliminating pseudo-edge points is proposed. This effectively filters out pseudo-edge points and obtains accurate elliptical parameters. Finally, based on these elliptical parameters, a monocular vision ranging model is established to solve the relative position between the USV and the buoy. The USV’s position from satellite observation is then fused with the relative position calculated using the method proposed in this paper to estimate the coordinates of the buoy in the geodetic coordinate system. Simulation experiments analyzed the impact of pixel noise, camera height, focal length, and rotation angle on localization accuracy. The results show that within a range of 40 m in width and 80 m in length, the coordinates calculated by this method have an average absolute error of less than 1.2 m; field experiments on actual ships show that the average absolute error remains stable within 2.57 m. This method addresses the positioning issues caused by disturbances such as reflections, water obstruction, and changes in illumination, achieving a positioning accuracy comparable to that of general satellite positioning. Full article

The autonomous decision-making model for ship navigation requires extensive interaction and trial-and-error in real, complex environments to ensure optimal decision-making performance and efficiency across various scenarios. However, existing approaches still encounter significant challenges in addressing the temporal features of state space and tackling complex dynamic collision avoidance tasks, primarily due to factors such as environmental uncertainty, the high dimensionality of the state space, and limited decision robustness. This paper proposes an adaptive temporal decision-making model based on reinforcement learning, which utilizes Long Short-Term Memory (LSTM) networks to capture temporal features of the state space. The model integrates an enhanced Proximal Policy Optimization (PPO) algorithm for efficient policy iteration optimization. Additionally, a simulation training environment is constructed, incorporating multi-factor coupled physical properties and ship dynamics equations. The environment maps variables such as wind speed, current velocity, and wave height, along with dynamic ship parameters, while considering the International Regulations for Preventing Collisions at Sea (COLREGs) in training the autonomous navigation decision-making model. Experimental results demonstrate that, compared to other neural network-based reinforcement learning methods, the proposed model excels in environmental adaptability, collision avoidance success rate, navigation stability, and trajectory optimization. The model’s decision resilience and state-space mapping align with real-world navigation scenarios, significantly improving the autonomous decision-making capability of ships in dynamic sea conditions and providing critical support for the advancement of intelligent shipping. Full article

Collision avoidance algorithms play a crucial role in ensuring the safety and effectiveness of autonomous ships, which require comprehensive testing in realistic multi-ship encounter scenarios. However, existing scenario generation methods often inadequately represent the spatiotemporal complexity and dynamic risk interactions of real-world encounters, leading to biased evaluations. To bridge this gap, this paper proposes a combinatorial-testing-based scenario generation framework integrated with spatiotemporal complexity optimisation. First, a full-process scenario representation model is developed by abstracting real-world navigation features into a discretised parameter space. Subsequently, a combinatorial-testing-based scenario generation method is adopted to cover the parameter space, generating a high-coverage scenario set. Finally, spatiotemporal complexity is introduced to filter out oversimplified scenarios and extremely dangerous scenarios. Experiments demonstrated that 13.7% of generated scenarios were eliminated as unrealistic or trivial, while high-risk encounter scenarios and multi-ship interaction scenarios were amplified by 7.96 times and 5.84 times, respectively. Compared to conventional methods, the optimised scenario set exhibited superior alignment with real-world complexity, including dynamic risk escalation and multi-ship coordination challenges. The proposed framework not only advances scenario generation methodology through its integration of combinatorial testing and complexity-driven optimisation, but also provides a practical tool for rigorously validating autonomous ship safety systems. Full article

Intelligent ships are a key focus for the future development of maritime transportation, relying on efficient decision-making and autonomous control within complex environments. To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. The study specifically targets the extraction and modeling of critical events, including variations in ship speed, course changes, vessel encounters, and port entries and exits. To evaluate the urgency of encounters, mathematical algorithms are applied to the Distance to Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA) metrics. Furthermore, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to identify suitable docking berths. Additionally, multi-source meteorological data are integrated with ship dynamic data, providing a more comprehensive representation of the maritime environment. The resulting knowledge system effectively combines ship attributes, navigational status, event relationships, and environmental factors, thereby offering a robust framework for supporting intelligent ship operations. Full article

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions. Full article

Path planning and collision avoidance issues are key to the autonomous navigation of unmanned surface vehicles (USVs). This study proposes an adaptive differential evolution algorithm model integrated with the analytic hierarchy process (AHP-ADE). The traditional differential evolution algorithm is enhanced by introducing an elite archive strategy and adaptively adjusting the scale factor F and the crossover factor $CR$ to balance global and local search capabilities, preventing premature convergence and improving the search accuracy. Additionally, the collision risk index (CRI) model is optimized and combined with the quaternion ship domain, enhancing the precision of CRI calculations and USV autonomous collision avoidance capabilities. The improved CRI model, the International Regulations for Preventing Collisions at Sea, and the optimal collision avoidance distance were incorporated as evaluation factors in a fitness function assessment, with weights determined through the AHP to enhance the rationality and accuracy of the fitness function. The proposed AHP-ADE algorithm was compared with the improved particle swarm algorithm, and the performance of the algorithm was comprehensively evaluated using safety, economy, and operational efficiency. Simulation experiments on the MATLAB platform demonstrated that the proposed AHP-ADE algorithm exhibited better performance in scenarios involving multiple ship encounters, thus proving its effectiveness. Full article

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios. Full article