Real-time Ship Research Articles

AQSFormer: Adaptive Query Selection Transformer for Real-Time Ship Detection from Visual Images

The Internet of Things (IoT) has emerged as a popular topic in both industrial and academic research. IoT devices are often equipped with rapid response capabilities to ensure seamless communication and interoperability, showing significant potential for IoT-based maritime traffic monitoring and navigation safety tasks. However, this also presents major challenges for maritime surveillance systems. The diversity of IoT devices and variability in collected data are substantial. Visual image ship detection is crucial for maritime tasks, yet it must contend with environmental challenges such as haze and waves that can obscure ship details. To address these challenges, we propose an adaptive query selection transformer (AQSFormer) that utilizes two-dimensional rotational position encoding for absolute positioning and integrates relative positions into the self-attention mechanism to overcome insensitivity to the position. Additionally, the introduced deformable attention module focuses on ship edges, enhancing the feature space resolution. The adaptive query selection module ensures a high recall rate and a high end-to-end processing efficiency. Our method improves the mean average precision to 0.779 and achieves a processing speed of 31.3 frames per second, significantly enhancing both the real-time capabilities and accuracy, proving its effectiveness in ship detection.

A High-Precision Inverse Finite Element Method for Shape Sensing and Structural Health Monitoring

In the contemporary era, the further exploitation of deep-sea resources has led to a significant expansion of the role of ships in numerous domains, such as in oil and gas extraction. However, the harsh marine environments to which ships are frequently subjected can result in structural failures. In order to ensure the safety of the crew and the ship, and to reduce the costs associated with such failures, it is imperative to utilise a structural health monitoring (SHM) system to monitor the ship in real time. Displacement reconstruction is one of the main objectives of SHM, and the inverse finite element method (iFEM) is a powerful SHM method for the full-field displacement reconstruction of plate and shell structures. However, existing inverse shell elements applied to curved shell structures with irregular geometry or large curvature may result in element distortion. This paper proposes a high-precision iFEM for curved shell structures that does not alter the displacement mode of the element or increase the mesh and node quantities. In reality, it just modifies the methods of calculation. This method is based on the establishment of a local coordinate system on the Gaussian integration point and the subsequent alteration of the stiffness integration. The results of numerical examples demonstrate that the high-precision iFEM is capable of effectively reducing the displacement difference resulting from inverse finite element method reconstruction. Furthermore, it performs well in practical engineering applications.

Enhancing Inland Waterway Safety and Management through Machine Learning-Based Ship Detection

Efficient ship detection is essential for inland waterway management. Recent advances in artificial intelligence have prompted research in this field. This study introduces a real-time ship detection model utilizing computer vision and the YOLO object detection framework. The model is designed to identify and locate common inland waterway vessels, such as container ships, passenger vessels, barges, ferries, canoes, fishing boats, and sailboats. Data augmentation techniques were employed to enhance the model's ability to handle variations in ship appearance, weather, and image quality. The system achieved a mean Average Precision (mAP) of 98.4%, with precision and recall rates of 96.6% and 95.0%, respectively. These results demonstrate the model's effectiveness in practical applications. Its ability to generalize across diverse vessel types and environmental conditions suggests its potential integration into video surveillance for improved maritime safety, traffic control, and search and rescue operations.

YOLOSAR-Lite: a lightweight framework for real-time ship detection in SAR imagery

ABSTRACT Real-time ship detection using SAR images is crucial for maritime operations but remains challenging due to issues such as sidelobes, complex background interferences, and target defocusing. Studies have shown that deep learning can address these problems, current deep learning algorithms often suffer from achieving the necessary accuracy due to complex noise and background characteristics. Additionally, advanced algorithms excelling in feature extraction usually require significant computational resources. This paper proposes a novel deep learning approach based on YOLOv8 to improve accuracy while reducing model complexity and computational burden. The approach innovatively integrates a knowledge distillation module into the YOLOv8. We further enhance the model by replacing the original YOLOv8 backbone, neck, and head with lightweight alternatives: HGNetv2, SlimNeck, and a newly designed decoupled head. The enhanced model, YOLOSAR-Lite, demonstrated exceptional performance on both accuracy and model complexity metrics. Rigorous evaluation on the Official-SSDD dataset and the SAR-Ship-Dataset revealed recognition accuracies of 95.32% and 96.06%, respectively. Furthermore, YOLOSAR-Lite achieved significant reduction in complexity compared to the YOLOv8: 4.48G FLOPs (45.30% reduction), 2.05M parameters (31.89% reduction), and 3.91MB model size (31.76% reduction). These improvements demonstrate YOLOSAR-Lite achieves higher accuracy with minimal complexity. Consequently, YOLOSAR-Lite is a promising solution for real-time ship detection.

An Improved YOLOv8 OBB Model for Ship Detection through Stable Diffusion Data Augmentation.

Unmanned aerial vehicles (UAVs) with cameras offer extensive monitoring capabilities and exceptional maneuverability, making them ideal for real-time ship detection and effective ship management. However, ship detection by camera-equipped UAVs faces challenges when it comes to multi-viewpoints, multi-scales, environmental variability, and dataset scarcity. To overcome these challenges, we proposed a data augmentation method based on stable diffusion to generate new images for expanding the dataset. Additionally, we improve the YOLOv8n OBB model by incorporating the BiFPN structure and EMA module, enhancing its ability to detect multi-viewpoint and multi-scale ship instances. Through multiple comparative experiments, we evaluated the effectiveness of our proposed data augmentation method and the improved model. The results indicated that our proposed data augmentation method is effective for low-volume datasets with complex object features. The YOLOv8n-BiFPN-EMA OBB model we proposed performed well in detecting multi-viewpoint and multi-scale ship instances, achieving the mAP (@0.5) of 92.3%, the mAP (@0.5:0.95) of 77.5%, a reduction of 0.8 million in model parameters, and a detection speed that satisfies real-time ship detection requirements.

Peningkatan Akurasi Pada Sistem Monitoring Posisi Kapal Menggunakan Metode Kalman Filter

This research produces a monitoring system to determine the position of the ship using the Kalman Filter method in web-based system development using the waterfall development method. This system allows users to view the position and coordinates of the ship in real-time. By applying the Kalman Filter method, uncertainty and noise in measuring the position of the ship can be reduced, thereby increasing the accuracy in determining the actual position of the ship. The system development process is carried out using the waterfall method which consists of requirements analysis, design, implementation, testing, and maintenance. The developed web-based system provides more accurate and reliable information to users, with the ability to view the position and coordinates of the ship in real time. This research contributes to the development of a more effective ship position monitoring system that can be used in various purposes such as navigation, surveillance, and ship monitoring.

Integration of Technology and Regulations for Safe and Efficient Marine Logistics

Maritime logistics is at the heart of global trade, with nearly 90% of the world's cargo transported by sea. However, the industry faces significant challenges in ensuring safety, operational efficiency, and environmental sustainability amidst increasing trade volumes and demands to reduce carbon emissions. Integration of modern technology with existing regulatory frameworks is key to improving maritime logistics performance while complying with safety and environmental standards. This paper explores various current technologies that can be applied, such as real-time ship tracking systems with the Internet of Things (IoT) and wireless communications, automation of loading and unloading processes using robotics and artificial intelligence (AI), as well as the use of big data and predictive analytics for optimization route and fleet management. These technologies have the potential to increase supply chain visibility, and time efficiency, reduce human error, and optimize resources. On the other hand, international regulatory frameworks such as the Convention on the Safety of Life at Sea (SOLAS) and the Convention for the Prevention of Pollution from Ships (MARPOL) from the International Maritime Organization (IMO), as well as national and regional regulations related to shipping safety, the marine environment, maritime security, and labor, must be adhered to in adopting new technology.

A novel regional ship collision risk assessment framework for multi-ship encounters in complex waters

To address the issue of precisely quantifying ship collision risk and identifying collision hotspots in complex waters, this study proposed a regional ship collision risk assessment framework that includes a multi-ship encounter recognition model, a multi-ship comprehensive collision risk assessment model, and a collision hotspot recognition model. Firstly, the directed distance density-based spatial clustering of applications with noise (DBSCAN) algorithm was proposed. Based on this algorithm, a multi-ship encounter recognition model was established to cluster ship positions and obtain ship encounter clusters. Subsequently, the multi-ship comprehensive collision risk assessment model was constructed for multi-ship encounter situations based on the hierarchical classification method, enabling the determination of each ship's Comprehensive Collision Risk Value (CCRV). Finally, the collision hotspot recognition model was built using the grid analysis method, which can identify areas with a higher risk of ship collision. The automatic identification system (AIS) data of the Yangtze River estuary were used to validate the effectiveness of our framework. The results show that our framework can accurately measure ship collisions risks and identify areas with high collision risks, offering valuable insights to maritime authorities, providing actionable insights for real-time ship collision risk monitoring.

Peran Teknologi Informasi dalam Transformasi di Industri Perkapalan

Management transformation in the shipping industry has become increasingly important with the advancement of information technology. This research aims to analyze the role of information technology in management transformation in the shipping industry. Through a literature review approach, this study explores the use of information technology in enhancing operational efficiency, facilitating faster decision-making, and improving company competitiveness. The main findings indicate that information technology has been a key driver in management transformation, with the adoption of integrated logistics management systems and real-time ship monitoring. However, challenges such as infrastructure complexity, data security issues, and resource constraints are also identified in the implementation of information technology. Effective mitigation strategies are needed to address these challenges. This study also highlights the importance of collaboration and partnerships in the transformation of the shipping industry. Thus, this research provides a comprehensive understanding of the role of information technology in management transformation in the shipping industry, while highlighting the challenges faced and their practical implications.

An Attention-Averaging-Based Compression Algorithm for Real-Time Transmission of Ship Data via Beidou Navigation System

The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression of such data. Therefore, this paper introduces an algorithm tailored for real-time compression of sequential ship status data. The algorithm is engineered to ensure both accuracy and the preservation of valid data range integrity. Our methodology integrates quantization, predictive coding employing an attention-averaging-based predictor, and arithmetic coding. This combined approach facilitates the transmission of succinct messages through the BeiDou Navigation System, enabling the live monitoring of ocean-going vessels. Experimental trials conducted with authentic data obtained from ship monitoring systems validate the efficiency of our approach. The achieved compression rates closely approximate theoretical minimum values. Consequently, this method exhibits substantial promise for the real-time transmission of parameters across various systems.

YOLOSeaShip: a lightweight model for real-time ship detection

ABSTRACT With the rapid advancements in computer vision, ship detection models based on deep learning have been more and more prevalent. However, most network methods use expensive costs with high hardware equipment needed to increase detection accuracy. In response to this challenge, a lightweight real-time detection approach called YOLOSeaShip is proposed. Firstly, derived from the YOLOv7-tiny model, the partial convolution was utilized to replace the original 3×1 convolution in the ELAN module to further fewer parameters and improve the operation speed. Secondly, the parameter-free average attention module was integrated to improve the locating capacity for the hull of a ship in an image. Finally, the accuracy changes of the Focal EIoU hybrid loss function under different parameter changes were studied. The practical results trained on the SeaShips (7000) dataset demonstrate that the suggested method can detect and classify the ship position from the image more efficiently, with mAP of 0.976 and FPS of 119.84, which is ideal for real-time ship detection applications.

Robust and Real-time Ship Object Detection Method Based on Enhanced CNN

Robust and Real-time Ship Object Detection Method Based on Enhanced CNN

A novel method for the evaluation of ship berthing risk using AIS data

The assessment of ship berthing risk, encompassing the potential for berthing collisions and unforeseen events, holds paramount importance in the realm of waterway traffic management and maritime surveillance. However, existing methods for analyzing ship berthing risk suffer from limitations in terms of timeliness, comprehensiveness and data accessibility. Therefore, this paper presents a novel approach to ship berthing risk assessment. The proposed method relies on Automatic Identification System (AIS) data and takes into consideration information related to the ship, berth, and environmental factors. It calculates crucial parameters, including the vertical distance between the ship and the berth, berthing speed, berthing angle and real-time distance between the ship and the berth, utilizing the AIS data and the berth location. Furthermore, environmental disturbance data pertaining to the ship's berthing environment is integrated with AIS data. Subsequently, we introduce the Improved Bossel Model considering Catastrophe (IBM-CC) to evaluate ship berthing risk in real-time. Finally, the proposed method was validated using actual ship berthing data and various simulation scenarios. The results demonstrate that our proposed method accurately assesses real-time ship berthing risk under diverse scenarios, offering a novel approach for the real-time and precise quantitative assessment of ship berthing risk.

Construction of a Real-Time Ship Trajectory Prediction Model Based on Ship Automatic Identification System Data

The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model’s reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model.

A Lightweight Model for Real-Time Monitoring of Ships

Real-time monitoring of ships is crucial for inland navigation management. Under complex conditions, it is difficult to balance accuracy, real-time performance, and practicality in ship detection and tracking. We propose a lightweight model, YOLOv8-FAS, to address this issue for real-time ship detection and tracking. First, FasterNet and the attention mechanism are integrated and introduced to achieve feature extraction simply and efficiently. Second, the lightweight GSConv convolution method and a one-shot aggregation module are introduced to construct an efficient network neck to enhance feature extraction and fusion. Furthermore, the loss function is improved based on ship characteristics to make the model more suitable for ship datasets. Finally, the advanced Bytetrack tracke is added to achieve the real-time detection and tracking of ship targets. Compared to the YOLOv8 model, YOLOv8-FAS reduces computational complexity by 0.8×109 terms of FLOPs and reduces model parameters by 20%, resulting in only 2.4×106 parameters. The mAP-0.5 is improved by 0.9%, reaching 98.50%, and the real-time object tracking precision of the model surpasses 88%. The YOLOv8-FAS model combines light weight with high precision, and can accurately perform ship detection and tracking tasks in real time. Moreover, it is suitable for deployment on hardware resource-limited devices such as unmanned surface ships.

Model Experimental Study on a T-Foil Control Method with Anti-Vertical Motion Optimization of the Mono Hull

T-foils with active control systems can adjust their attack angle according to the movement of the ship in real time, providing higher lift force and improving the seakeeping performance of a ship. The optimization of the control signal and that of the control method have an important influence on the effect of active T-foils. In this paper, the control method of the T-foil’s swinging angle is established and optimized on the basis of model testing in order to increase the effect of the T-foil. First, the governing equation is introduced by establishing the proportional relationship between the angular motion of the hull and the lift moment of the T-foil. On the basis of the model of the T-foil’s lift force, the governing equation of the T-foil’s swinging angle is deduced and simplified using the test results of the ship model with a passive T-foil and without a T-foil. Then, the active T-foil control system is established by comparing the effects of T-foils with different control signals. Finally, the efficacies of the passive and active T-foil are reported and discussed. It is found that the pitch angular velocity is a more appropriate signal than the pitch angle and pitch angular acceleration. T-foils with pitch angular velocity control can decrease the vertical motion response in the resonance region of a ship’s encounter frequency by more than about 20% compared to the case of the bare ship model, while also increasing the anti-bow acceleration effect by more than 15% compared to the case of passive control. The results obtained by model testing have a certain guiding significance for specific engineering practices.

YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection

Automatic ship detection in SAR images plays an essential role in both military and civilian fields. However, most of the existing deep learning detection methods introduce complex models and huge calculations while improving the detection accuracy, which is not conducive to the application of real-time ship detection. To solve this problem, an efficient lightweight network YOLO-Lite is proposed for SAR ship detection in this paper. First, a lightweight feature enhancement backbone (LFEBNet) is designed to reduce the amount of calculation. Additionally, a channel and position enhancement attention (CPEA) module is constructed and embedded into the backbone network to more accurately locate the target location by capturing the positional information. Second, an enhanced spatial pyramid pooling (EnSPP) module is customized to enhance the expression ability of features and address the position information loss of small SAR ships in high-level features. Third, we construct an effective multi-scale feature fusion network (MFFNet) with two feature fusion channels to obtain feature maps with more position and semantic information. Furthermore, a novel confidence loss function is proposed to effectively improve the SAR ship target detection accuracy. Extensive experiments on SSDD and SAR ship datasets verify the effectiveness of our YOLO-Lite, which can not only accurately detect SAR ships in different backgrounds but can also realize a lightweight architecture with low computation cost.

Orientation-aware ship detection via a rotation feature decoupling supported deep learning approach

Ship imaging position plays an important role in visual navigation, and thus significant focuses have been paid to accurately extract ship imaging positions in maritime videos. Previous studies are mainly conducted in the horizontal ship detection manner from maritime image sequences. This can lead to unsatisfied ship detection performance due to that some background pixels maybe wrongly identified as ship contours. To address the issue, we propose a novel rotational you only look once (YOLO) based model (RYM) to accurately yet fast detect ships from maritime images by considering ship rotation angle. The proposed RYM model are designed to detect tilted ships from images with the help of a rotation decoupled (RD) head, attentional mechanism and bidirectional feature network (BiFPN). The experimental results suggested that RYM can obtain satisfied ship detection performance considering that average accuracy reaches 96.7%. The precision and recall indicators are 93.2% and 94.7%, respectively. The proposed framework can be applied into real-time ship detection task due to that the processing speed is 45.6 frames per second (FPS).

Optimized Turning Basin Design for Inland Waterways

Concept design guidelines for ship turning basins are often not met in practice due to environmental, economic, or geotechnical restrictions. In these cases, a detailed design procedure is followed to develop an optimized turning basin geometry within the environmental constraints. Then, real-time ship maneuvering simulations are used to evaluate the maneuvers in the proposed geometry. However, such a detailed design procedure is costly, especially if a large number of simulations are required. To restrict the number of simulations, the waterway authority [Voies navigables de France (VNF)] ordered a study to find a generic and optimal design for a turning basin which could be suitable for the upgrade in the northern part of their inland waterway network. Therefore, the turning maneuvers for two typical inland navigation vessels were simulated in real time under different hydrometeorological conditions. This study was conducted in three stages. First, maneuvers in traditional circular and trapezoidal turning basins of different sizes were executed and analyzed. Second, optimized turning basin geometries were developed. Finally, the turning maneuvers in those optimized geometries were analyzed. Based on the results of this analysis, a stepwise approach was proposed to select an optimized turning basin geometry that was based on the conditions present on site. This stepwise approach for the design of turning basins could reduce the real-time simulations that are required when upgrading a waterway network.

Research into Ship Trajectory Prediction Based on An Improved LSTM Network

The establishment of ship trajectory prediction is critical in analyzing trajectory data. It serves as a critical reference point for identifying abnormal behavior and potential collision risks for ships. Accurate and real-time ship trajectory prediction is essential during navigation. Since the timing of automatic identification system (AIS) data is irregular, traditional methods usually use time calibration to simulate the data of uniform sequencing before analysis. Inevitably, this increases the chances of error and time delays. To address this issue, we propose a time-aware LSTM (T-LSTM) single-ship trajectory model combined with the generative adversarial network (GAN) to predict multiple ship trajectories. These analysis methods are capable of directly analyzing AIS data and have demonstrated better performance in both single-ship and multi-ship trajectories. Our experimental results show that the proposed method achieves high accuracy and can meet the practical navigation requirements of ships.