Search Results (15)

Despite advancements in direct sensing technologies, accurately capturing complex wave–structure interactions remain a significant challenge in ship and ocean engineering. Ensuring the safety and reliability of floating structures requires precise monitoring of dynamic water interactions, particularly in extreme sea conditions. Recent developments in computer vision and artificial intelligence have enabled advanced image-based sensing techniques that complement traditional measurement methods. This study investigates the application of Computerized Video Analysis (CVA) for water surface tracking in maritime experimental tests, marking the first exploration of digitalized experimental video analysis at the Australian Maritime College (AMC). The objective is to integrate CVA into laboratory data acquisition systems, enhancing the accuracy and robustness of wave interaction measurements. A novel algorithm was developed to track water surfaces near floating structures, with its effectiveness assessed through a Wave Energy Converter (WEC) experiment. The method successfully captured wave runup interactions with the hull form, operating alongside traditional sensors to evaluate spectral responses at a wave height of 0.4 m. Moreover, its application in irregular wave conditions demonstrated the algorithm’s capability to reliably detect the waterline across varying wave heights and periods. The findings highlight CVA as a reliable and scalable approach for improving safety assessments in maritime structures. Beyond controlled laboratory environments, this method holds potential for real-world applications in offshore wind turbines, floating platforms, and ship stability monitoring, contributing to enhanced structural reliability under operational and extreme sea states. Full article

In complex marine environments, accurate prediction of maneuvering motion is crucial for the precise control of intelligent ships. This study aims to enhance the predictive capabilities of maneuvering motion for intelligent ships in such environments. We propose a novel maneuvering motion prediction method based on Long Short-Term Memory (LSTM) and Multi-Head Attention Mechanisms (MHAM). To construct a foundational dataset, we integrate Computational Fluid Dynamics (CFD) numerical simulation technology to develop a mathematical model of actual ship maneuvering motions influenced by wind, waves, and currents. We simulate typical operating conditions to acquire relevant data. To emulate real marine environmental noise and data loss phenomena, we introduce Ornstein–Uhlenbeck (OU) noise and random occlusion noise into the data and apply the MaxAbsScaler method for dataset normalization. Subsequently, we develop a black-box model for intelligent ship maneuvering motion prediction based on LSTM networks and Multi-Head Attention Mechanisms. We conduct a comprehensive analysis and discussion of the model structure and hyperparameters, iteratively optimize the model, and compare the optimized model with standalone LSTM and MHAM approaches. Finally, we perform generalization testing on the optimized motion prediction model using test sets for zigzag and turning conditions. The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance. Full article

The high salinity, humidity, and oxygen-rich environments of coastal marine areas pose serious corrosion risks to metal structures, particularly in equipment such as ships, offshore platforms, and port facilities. With the development of artificial intelligence technologies, image recognition-based intelligent detection methods have provided effective support for corrosion monitoring in marine engineering structures. This study aims to explore the performance improvements of different modified YOLOv5 models in small-object corrosion detection tasks, focusing on five IoU-based improved loss functions and their optimization effects on the YOLOv5 model. First, the study utilizes corrosion testing data from the Zhoushan seawater station of the China National Materials Corrosion and Protection Science Data Center to construct a corrosion image dataset containing 1266 labeled images. Then, based on the improved IoU loss functions, five YOLOv5 models were constructed: YOLOv5-NWD, YOLOv5-Shape-IoU, YOLOv5-WIoU, YOLOv5-Focal-EIoU, and YOLOv5-SIoU. These models, along with the traditional YOLOv5 model, were trained using the dataset, and their performance was evaluated using metrics such as precision, recall, F1 score, and FPS. The results showed that YOLOv5-NWD performed the best across all metrics, with a 7.2% increase in precision and a 2.2% increase in F1 score. The YOLOv5-Shape-IoU model followed, with improvements of 4.5% in precision and 2.6% in F1 score. In contrast, the performance improvements of YOLOv5-Focal-EIoU, YOLOv5-SIoU, and YOLOv5-WIoU were more limited. Further analysis revealed that different IoU ratios significantly affected the performance of the YOLOv5-NWD model. Experiments showed that the 4:6 ratio yielded the highest precision, while the 6:4 ratio performed the best in terms of recall, F1 score, and confusion matrix results. In addition, this study conducted an assessment using four datasets of different sizes: 300, 600, 900, and 1266 images. The results indicate that increasing the size of the training dataset enables the model to find a better balance between precision and recall, that is, a higher F1 score, while also effectively improving the model’s processing speed. Therefore, the choice of an appropriate IoU ratio should be based on specific application needs to optimize model performance. This study provides theoretical support for small-object corrosion detection tasks, advances the development of loss function design, and enhances the detection accuracy and reliability of YOLOv5 in practical applications. Full article

This study is aimed to improve the intelligence level, efficiency, and accuracy of ship safety and security systems by contributing to the development of marine weather forecasting. The accurate and prompt recognition of weather fax charts is very important for navigation safety. This study employed many artificial intelligent (AI) methods including a vectorization approach and target recognition algorithm to automatically detect the severe weather information from Japanese and US weather charts. This enabled the expansion of an existing auto-response marine forecasting system’s applications toward north Pacific and Atlantic Oceans, thus enhancing decision-making capabilities and response measures for sailing ships at actual sea. The OpenCV image processing method and YOLOv5s/YOLO8vn algorithm were utilized to make template matches and locate warning symbols and weather reports from surface weather charts. After these improvements, the average accuracy of the model significantly increased from 0.920 to 0.928, and the detection rate of a single image reached a maximum of 1.2 ms. Additionally, OCR technology was applied to retract texts from weather reports and highlighted the marine areas where dense fog and great wind conditions are likely to occur. Finally, the field tests confirmed that this auto and intelligent system could assist the navigator within 2–3 min and thus greatly enhance the navigation safety in specific areas in the sailing routes with minor text-based communication costs. Full article

The ability to classify and recognize maritime targets based on visual images plays an important role in advancing ship intelligence and digitalization. The current target recognition algorithms for common maritime targets, such as buoys, reefs, other ships, and bridges of different colors, face challenges such as incomplete classification, low recognition accuracy, and a large number of model parameters. To address these issues, this paper proposes a novel maritime target recognition method called DTI-YOLO (DualConv Triple Attention InnerEIOU-You Only Look Once). This method is based on a triple attention mechanism designed to enhance the model’s ability to classify and recognize buoys of different colors in the channel while also making the feature extraction network more lightweight. First, the lightweight double convolution kernel feature extraction layer is constructed using group convolution technology to replace the Conv structure of YOLOv9 (You Only Look Once Version 9), effectively reducing the number of parameters in the original model. Second, an improved three-branch structure is designed to capture cross-dimensional interactions of input image features. This structure forms a triple attention mechanism that accounts for the mutual dependencies between input channels and spatial positions, allowing for the calculation of attention weights for targets such as bridges, buoys, and other ships. Finally, InnerEIoU is used to replace CIoU to improve the loss function, thereby optimizing loss regression for targets with large scale differences. To verify the effectiveness of these algorithmic improvements, the DTI-YOLO algorithm was tested on a self-made dataset of 2300 ship navigation images. The experimental results show that the average accuracy of this method in identifying seven types of targets—including buoys, bridges, islands and reefs, container ships, bulk carriers, passenger ships, and other ships—reached 92.1%, with a 12% reduction in the number of parameters. This enhancement improves the model’s ability to recognize and distinguish different targets and buoy colors. Full article

Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability. Full article

A hierarchical population task planning method is presented to enhance the test efficiency and reliability of intelligent technology test ships under various tasks and complex limitations. Firstly, a mathematical model of the vehicle path problem for multi-voyage vessel testing is developed, which aims to minimize the ship’s fixed and fuel costs, taking into account the energy and space constraints of an intelligent technology test vessel, as well as practical factors such as the dependencies and temporal relationships between test tasks. Second, to fairly minimize constraint complexity in the planning process, an offshore test task planning architecture based on the concept of hierarchical population is explored and built. This architecture separates task planning into four levels and allocates the tasks to distinct populations. Using this information, a grouping genetic algorithm is suggested based on the characteristics of the population. This algorithm uses a unique coding method to represent task clusters and narrows the range of possible solutions. The issue of the conventional grouping genetic algorithm’s vast search space is resolved. Lastly, simulation verification is carried out, and the results show that the method can effectively solve the problem of offshore test task planning for intelligent technology test ships under multi-constraint conditions. It reduces test cost and improves test efficiency. Full article

With the global advancement of maritime autonomous surface ships (MASS), the critical task of verifying their key technologies, particularly in challenging conditions, becomes paramount. This study introduces a synthetic maritime traffic generation system (S-MTGS) designed for the efficient and safe verification of these technologies. The S-MTGS encompasses a maritime traffic generator integrating a generator based on absolute position (GAP) and a generator based on relative position (GRP). This innovative system leverages historical maritime data to create various scenarios or generate virtual ships based on their interactions with and proximity to other ships. The virtual ships adeptly navigate and perform collision avoidance maneuvers with nearby vessels enabled by the integrated collision avoidance algorithm. The S-MTGS’s ability to generate a wide range of maritime traffic information mirroring actual maritime conditions is pivotal for thoroughly verifying the performance of MASS technology under both standard and extreme situations. The development of the S-MTGS represents a significant advancement in maritime safety and technology. It can evaluate collision avoidance and navigation systems in MASS, featuring a virtual environment for realistic scenario testing and an intelligent navigation system focused on route tracking and collision avoidance. Full article

Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared (IR) data exhibit rich feature space distribution, resulting in performance variations among SIATR models, thus preventing the existence of a universally superior model for all recognition scenarios. In light of this, this study proposes a model-matching method for SIATR tasks based on bipartite graph theory. This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. The objective is to selectively recommend the optimal candidate model for a given sample, enhancing the overall recognition performance and applicability of the model. We separately conducted tests for the optimization of accuracy and credibility on high-fidelity simulation data, achieving Accuracy and EDMS (our credibility metric) of 95.86% and 0.7781. Our method improves by 1.06% and 0.0274 for each metric compared to the best candidate models (six in total). Subsequently, we created a recommendation system that balances two tasks, resulting in improvements of 0.43% (accuracy) and 0.0071 (EDMS). Additionally, considering the relationship between model resources and performance, we achieved a 28.35% reduction in memory usage while realizing enhancements of 0.33% (accuracy) and 0.0045 (EDMS). Full article

The construction of New Business Studies (NBS) in China and big data technology offer an opportunity for teaching reform. Based on the existing teaching resources, professional knowledge, data, and technology, we monitored the dynamics and checked the statistics of air pollutant emissions from ships in global waters. Various techniques of big data analysis and methods of artificial intelligence were employed, including data collection, data fusion, feature analysis, deep learning network, and system testing. Specifically, the scenario of green shipping monitoring was reproduced by virtual reality; experimental learning was carried out, involving five experimental methods, eight experimental steps, and ten interactive operations; and the results of the experimental learning were assessed. In this way, the students had a better cognition of datasets, a deeper understanding of data correlation, and an improved mastery of interactive operations. In addition, the students varied in terms of learning performance, experimental participation, and active performance inspired by individual thinking. Overall, the students were satisfied with the quality of experimental learning. Full article

The development of artificial intelligence (AI) technologies, such as machine learning algorithms, computer vision systems, and sensors, has allowed maritime autonomous surface ships (MASS) to navigate, detect and avoid obstacles, and make real-time decisions based on their environment. Despite the benefits of AI in MASS, its potential security threats must be considered. An adversarial attack is a security threat that involves manipulating the training data of a model to compromise its accuracy and reliability. This study focuses on security threats faced by a deep neural network-based object classification algorithm, particularly you only look once version 5 (YOLOv5), which is a model used for object classification. We performed transfer learning on YOLOv5 and tested various adversarial attack methods. We conducted experiments using four types of adversarial attack methods and parameter changes to determine the attacks that could be detrimental to YOLOv5. Through this study, we aim to raise awareness of the vulnerability of AI algorithms for object detection to adversarial attacks and emphasize the need for efforts to overcome them; these efforts can contribute to safe navigation in MASS. Full article

Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time. Full article

Maritime Autonomous Surface Ship (MASS) is promoted as the future of intelligent shipping. While autonomy technologies offer a solution for MASS, they have also resulted in new challenges for performance validation. To address this, a scenario-based validation method to test the autonomous collision avoidance system is proposed in this paper, including mining ship encounter scenarios from massive historical AIS data and randomly generated virtual test scenarios according to the parameter probability distributions from the collected real scenarios, as well as the final experiments: a total of 2900 generated scenarios including single ship and multi-ship encounter situations are created and applied to conduct testing experiments on the further assessment of our collision avoidance algorithm. The results indicate that the proposed method has the ability to quickly create appropriate testing scenarios according to AIS records, which are helpful to catch potential defects in a collision avoidance algorithm of MASS and to further analyze its navigating features. As a result, the research forms a systematic set of validation procedures from data gathering to practical experiments conduction, incorporating both the real statistics and the random generation method. Full article

Open-source intelligence (OSINT) tools are used for gathering information using different publicly available sources. With the rapid advancement in information technology and excessive use of social media in our daily lives, more public information sources are available than ever before. The access to public information from different sources can be used for unlawful purposes. Extracting relevant information from pools of massive public information sources is a large task. Multiple tools and techniques have been developed for this task, which can be used to identify people, aircraft, ships, satellites, and more. In this paper, we identify the tools used for extracting the OSINT information and their effectiveness concerning each other in different test cases. We mapped the identified tools with Cyber Kill Chain and used them in realistic cybersecurity scenarios to check their effusiveness in gathering OSINT. Full article

Since the e-Navigation strategy was put forward, various countries and regions in the world have researched e-Navigation test platforms. However, the sources of navigation data are multi-source, and there are still difficulties in the unified acquisition, processing, analysis and application of multi-source data. Users often find it difficult to obtain the required comprehensive navigation information. The purpose of this paper is to use e-Navigation architecture to design and develop maritime data security management platform, strengthen navigation safety guarantee, strengthen Marine environment monitoring, share navigation and safety information, improve the ability of shipping transportation organizations in ports, and protect the marine environment. Therefore, this paper proposes a four-layer system architecture based on Java 2 Platform Enterprise Edition (J2EE) technology, and designs a unified maritime data storage, analysis and management platform, which realizes the intelligent, visualized and modular management of maritime data at shipside and the shore. This platform can provide comprehensive data resource services for ship navigation and support the analysis and mining of maritime big data. This paper expounds on the design, development scheme and demonstration operation scheme of the maritime data security management platform from the system structure and data exchange mode. Full article