Improve Maritime Safety Research Articles

Rapid assessment of risks in tug operations within port areas

Summary: This article presents a rapid risk assessment methodology specifically designed for tug operations within port areas, addressing the unique challenges of these high-risk environments. Tug operations require efficient risk mitigation due to confined spaces, unpredictable environmental conditions, and potential human factors that may lead to incidents. Traditional, detailed risk assessment models are often impractical in real-time scenarios. Therefore, this study focuses on developing a streamlined, quantitative approach for assessing and managing risks in port towage. The paper introduces an enhanced Risk Factor (RF) model as a key component of the proposed methodology. Traditionally, the RF is calculated using the formula RF=P×C, where P denotes the probability of occurrence, and C represents the severity of the consequence. This study expands on the conventional formula by including two additional elements relevant to towage: E (Environmental Conditions) and H (Human Factors). Thus, the modified formula RF=P×C×E×H provides a more comprehensive risk profile for rapid assessment in dynamic port conditions. Each section of this paper systematically explores the variables of the extended RF model and applies it to common scenarios in port tug operations. The findings demonstrate that incorporating environmental and human factors into the RF calculation enhances the model’s reliability and relevance. Finally, the paper provides recommendations for implementing the proposed RF methodology, supporting port operators and tug crews in real-time risk assessment and decision-making, thereby ensuring safety outcomes, minimizing possible risks involved, and improving maritime safety overall

Physics-Based Abnormal Trajectory Gap Detection

Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and regulatory enforcement for global security concerns, such as illegal fishing, illegal oil transfers, and trans-shipments. The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data. The current literature on anomalous trajectory detection assumes linear interpolation within gaps, which may not be able to detect abnormal gaps since objects within a given region may have traveled away from their shortest path. In preliminary work, we introduced an abnormal gap measure that uses a classical space-time prism model to bound an object's possible movement during the trajectory gap and provided a scalable memoized gap detection algorithm (Memo-AGD). In this article, we propose a space time-aware gap detection (STAGD) approach to leverage space-time indexing and merging of trajectory gaps. We also incorporate a dynamic region merge-based (DRM) approach to efficiently compute gap abnormality scores. We provide theoretical proofs that both algorithms are correct and complete and also provide analysis of asymptotic time complexity. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves computation time over the baseline technique.

Visual Navigation Systems for Maritime Smart Ships: A Survey

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships.

Analyzing risk influencing factors of ship collision accidents: A data-driven Bayesian network model integrating physical knowledge

The interaction and propagation effects among risk-influencing factors (RIFs) often lead to maritime accidents. Therefore, to support maritime risk management in decision-making, it is imperative to conduct a quantitative risk assessment (QRA) of these accidents, which requires a methodology capable of capturing causal relationships in limited cases. To achieve this goal, this study proposes a data-driven Bayesian network (BN) model that integrates physical knowledge with the QRA. Based on the collected data, a combination of domain knowledge, structure learning, and parameter learning is employed to construct the model. In the data-driven phase, three structural learning algorithms including Bayesian search (BS), Greedy thick thinning (GTT), and Peter-Clark (PC) algorithms, were used in combination with the expectation maximization algorithm of parameter learning. Then, using four indices calculated by the confusion matrix, the most fitting algorithm for structure learning was chosen, and the confusion matrix was obtained by five-fold cross-validation. Finally, network propagation impact (NPI) is introduced to prioritize RIFs. Preventive measures and emergency plans are formulated based on the network resilience metric (NRM) and network vulnerability metric (NVM). This approach leverages the objectivity of a data-driven BN and integrates domain expertise to enhance model validity. A case study of collisions was conducted to demonstrate the applicability of the model. The data were sourced from 327 collision accident reports provided by the China Maritime Safety Administration, and the PC algorithm was chosen. Findings indicate that the “Management system”, “Supervision”, and “Crew training” RIFs are prioritized for preventive measures, whereas “Communication” receives more resources for emergency handling. The results suggest the formulation of risk management strategies for practitioners to improve maritime safety.

A risk prediction model for Maritime accidents

This study proposes analytical tools to predict maritime accidents involving dangerous goods to help improve maritime safety and preserve maritime and coastal heritage. Maritime accidents of dangerous goods can have devastating consequences, causing loss of life, damage to the environment and economic losses. There have been numerous studies attempting to predict maritime accidents, but most have focused on a single type of accident (e.g. oil spills) or a single region (e.g. Baltic Sea, Maritime Silk Road, etc.). This study takes a different approach, using a global dataset on maritime accidents of dangerous goods from 2010 to 2019 (that includes information on the type of casualty, the location, the amount of material released, the type of material released, the cause of the accident, and the outcome), it applies both a machine learning technique and a statistical approach based on the Fourier distribution of rare events as a dual approach to address the problem. Using the Tyrrhenian area as a case study, the results show that the proposed tools can predict the probability of an accident occurring with an acceptable level of accuracy. The paper can provide a valuable tool for decision makers and stakeholders, who can use the findings to identify regions at risk of maritime accidents and take measures to prevent their occurrence.

Developed Status and Forecast of Intelligent Ship Application

The concept of smart ships is not limited to ship autonomous driving, it covers a broader scope, including the full life cycle of ship design, construction, operation and maintenance. By integrating advanced sensors, navigation systems and data analysis platforms, smart ships are able to monitor ship status and the Marine environment in real time, optimize route planning and improve fuel efficiency, thereby reducing energy consumption and greenhouse gas emissions. In addition, smart ships can revolutionize the shipping industry by improving maritime safety and reducing human error through remote control and autonomous decision-making. In recent years, smart ship technology has gradually moved from the theoretical research stage to practical application, and has attracted wide attention from the global shipping industry. A number of international cooperation and research projects have been launched to explore the practical application potential and promotion paths of smart ships. However, the development of smart ships also faces many challenges, including technical difficulties, legal and regulatory restrictions, and public acceptance issues. This paper will deeply discuss the current development status of smart ships, analyze the main challenges it faces, and forecast the future development trend, in order to provide reference and inspiration for the innovative development of the shipping industry.

Optimal Routing and Scheduling of Flag State Control Officers in Maritime Transportation

Maritime transportation plays a pivotal role in the global merchandise trade. To improve maritime safety and protect the environment, every state must effectively control ships flying its flag, which is called flag state control (FSC). However, the existing FSC system is so inefficient that it cannot perform its intended function. In this study, we adopt an optimization method to tackle this problem by constructing an integer programming (IP) model to solve the FSC officer routing and scheduling problem, which aims to maximize the total weight of inspected ships with limited budget and human resources. Then we prove that the IP model can be reformulated into a partially relaxed IP model with the guarantee of the result optimality. Finally, we perform a case study using the Hong Kong port as an example. The results show that our model can be solved to optimality within one second at different scales of the problem, with the ship number ranging from 20 to 1000. Furthermore, our study can be extended by considering the arrangement of working timetables with finer granularity and the fatigue level of personnel.

Optimal path planning for a ship in coastal waters with deep Q network

With the development of autonomous ships, complete automation of passage planning has become an imminent priority. However, existing A* algorithms have the disadvantage of generating paths close to land because they prioritize minimizing the navigation distance. Therefore, this study proposes a method based on a deep Q network to effectively implement reward-and-penalty strategies considering required navigation areas and non-navigable areas throughout the port-to-port distance. First, the Busan and Gwangyang Ports were selected as the target areas and a container ship was selected as the target ship. Initially, non-navigable and reward areas were designated based on the water depth and electronic navigational chart information. Thereafter, we conducted experiments using algorithms in three types of environments: normal conditions, turbulent weather, and obstacle-involved environments. Furthermore, we employed the Douglas–Peucker algorithm to eliminate excessive waypoints. Experimental results demonstrated that the path planning of a ship obtained using a deep Q network involved more efficient and safer decisions for ship navigation. Furthermore, the navigation distance was reduced by 1.77% compared to the passage plan used by actual ships. The proposed approach is advantageous for automatically deriving the optimal mid-range path of ships and can thus contribute toward improving maritime safety and efficiency.

Predicting maritime accident risk using Automated Machine Learning

Machine learning (ML), particularly, Automated machine learning (AutoML) offers a range of possibilities for analysing large volumes of historical maritime accidents data with advanced algorithms for integrating predictive analytics in operational and policy decision-making for improving maritime safety. This study explores historical data of maritime accidents in Norwegian waters over 40 years. The data has been utilised for analysing five major maritime accident categories: grounding, contact damage, fire or explosion, collision, and heavy weather damage. A total of 29 classification ML algorithms were trained, and the Light Gradient Boosted Trees Classifier was found to be the best-performing with the highest predictive accuracy. The three most impactful factors for accident risk are the category of navigation waters, phase of operation, and gross tonnage of the vessel. Based on the feature effect results, vessels sailing in narrow coastal waters, in the along-the-way operational phase, and fishing vessels are highly vulnerable to grounding relative to other types of accidents. The results can be used as input for the entire procedure of risk analysis, from hazard identification to quantification of accident consequences, and the best-performing ML algorithm can be utilized in developing a decision support system for real-time maritime accident risk assessment.

Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS)

Abstract This article discusses the use of a deep learning neural network (DLNN) as a tool to improve maritime safety by classifying the potential threat to shipping posed by unexploded ordnance (UXO) objects. Unexploded ordnance poses a huge threat to maritime users, which is why navies and non-governmental organisations (NGOs) around the world are using dedicated advanced technologies to counter this threat. The measures taken by navies include mine countermeasure units (MCMVs) and mine-hunting technology, which relies on the use of sonar imagery to detect and classify dangerous objects. The modern mine-hunting technique is generally divided into three stages: detection and classification, identification, and neutralisation/disposal. The detection and classification stage is usually carried out using sonar mounted on the hull of a ship or on an underwater vehicle. There is now a strong trend to intensify the use of more advanced technologies, such as synthetic aperture sonar (SAS) for high-resolution data collection. Once the sonar data has been collected, military personnel examine the images of the seabed to detect targets and classify them as mine-like objects (MILCO) or non mine-like objects (NON-MILCO). Computer-aided detection (CAD), computer-aided classification (CAC) and automatic target recognition (ATR) algorithms have been introduced to reduce the burden on the technical operator and reduce post-mission analysis time. This article describes a target classification solution using a DLNN-based approach that can significantly reduce the time required for post-mission data analysis during underwater reconnaissance operations.

Enhancing maritime safety: A comprehensive review of challenges and opportunities in the domestic ferry sector

The domestic ferry sector is uniquely distinctive owing to its challenging navigation conditions and vulnerable accident records that have evoked endless calls for improving maritime safety. The recent adoption of IMO model safety regulations offers viable options for the Member States to standardize incorporation into national law. The operations of domestic ferries range from very large vessels with freight to small craft, which are often the only transport means for a large population of commuters in the developing world. The flexibility of domestic ferries is appealing; on the other hand, their operations are a challenge to handle, raising the need to identify those challenges that are incompatible with smooth operations and business opportunities. The maritime industry, specifically regarding domestic ferry operations, confronts multilayered challenges with direct implications on accident prevention and operational safety that necessitate a thorough analysis for a comprehensive understanding. This study explores five categories, namely, operations, technology and innovations, the human element, policy and regulation, and economics, recognized as pivotal to improving maritime safety. Our content analysis identifies the comprehensive taxonomies, that explain the current challenges and practical opportunities faced by the sector and which are notably lacking, urging efficient tenacity to ensure sustainable domestic ferry operations. The primary objective was to enhance safety standards, promoting sustainable shipping for all stakeholders involved. This study has identified 28 challenges and 90 opportunities, providing a significant pathway for sustainable decision-making that also adds value to the safety of the stakeholders. This study is expected to explore novel and fertile future research areas to promote scholarly discussion in the domestic ferry sector. Highlights Policy deficiency - Recognition of inadequate policy frameworks for domestic ferry operations in developing nations, necessitating immediate alignment with international safety standards Governance strengthening - There is an swift need for strong governance structures to address deficiencies in policy formulation, regulatory enforcement, and operational practices, ensuring effective safety oversight Standardization challenges - The absence of standardized definitions and categorizations hampers the development of uniform safety regulations within the domestic ferry sector Capacity building - Continuous training programs are necessary to foster a safety-conscious culture among the domestic ferry workforce Policy adaptation - Policies must adapt to technological advancements, and collaboration with international bodies is vital for enhancing safety standards within the domestic ferry sector

A hybrid prediction model of vessel trajectory based on attention mechanism and CNN-GRU

With the increase in global shipping volumes and the complexity of maritime transport systems, vessel trajectory prediction serves an important tool in improving maritime safety. However, most existing vessel trajectory prediction methods focus on a single feature and unable fuse high-dimensional features. To solve these problems, CNN-GRU model with a hybrid attention mechanism (AM) is proposed based on Automatic Identification System (AIS) data. First convolutional neural network (CNN) is proposed to extract the spatio-temporal information of the trajectory data. Then a gated recurrent unit (GRU) is designed to extract the temporal relationship of the trajectories. Finally, AM is introduced to learn the deep-level features and predict the vessel trajectories. To validate the effectiveness of the model, experiments are conducted on three real AIS datasets. In comparison with other models, the method has a high trajectory prediction accuracy.

Education And Training: Triangulation Of The Foundations Of Maritime Safety Improvement Policies

Maritime safety has been a primary concern for the Indonesian government since 1978 through the Minister of Transportation Decision No. KM. 53/OT/PHB-78. The formulation of Maritime Safety Improvement policies is carried out by identifying the phenomena regarding the urgency of improving maritime safety for the sustainability of both national and international shipping, grounded in legal products. The policy foundation is derived from three aspects: Maritime Safety Aspect, Manpower Management Aspect, and Education and Training Aspect. The result of this policy formulation gives birth to a new policy product, namely the Functional Sea Transportation Technical Training implemented by the Sea Transportation Education and Training Center (BP2TL). This research is a Policy Evaluation study using a qualitative approach by exploring three research focuses: the policy program's background, program objectives, and program targets. Data acquisition for the research is done through interview techniques, documentary studies, and literature reviews.

Analysing the Impact of Human Factors on Marine Accidents

Marine accidents remain a significant challenge for the maritime industry, with human factors identified as a leading cause. This study explores the most common human elements contributing to these incidents through descriptive and inferential analysis. Key factors include fatigue, which impairs decision-making; poor communication among crew and between ship and shore; and lack of situational awareness, often exacerbated by distractions or over-reliance on automation. Other critical contributors include inadequate training, decision-making errors, violations of procedures, substance use, and crew coordination issues. The findings underscore the complex interplay between individual behaviors, teamwork, and organizational practices in accident causation. Addressing these human factors through fatigue management, communication protocols, enhanced training, and adherence to safety procedures can significantly reduce risks. This study highlights the need for proactive interventions to improve maritime safety and minimize human error, reinforcing the importance of both organizational responsibility and individual accountability in high-stakes environments.

Review and Analysis of the Collision Incident between Cargo Ship Edmy and Fishing Vessel Tornado: Extracting Lessons from a Case

This study reviews an investigation report that involves collision incident between the Cargo Ship Edmy and the Fishing Vessel Tornado. After conducting a thorough analysis of the incident, it was concluded that the collision occurred due to a confluence of factors, namely excessive dependence on navigation equipment and inadequate utilisation of the navigation tools at hand. Consequently, a number of valuable insights have been identified with the aim of improving maritime safety. The study highlights the significance of evaluating and enhancing watchkeeping protocols. This encompasses the establishment of the minimum prerequisites for bridge configuration and surveillance in diverse sailing situations, with the aim of guaranteeing sufficient attentiveness and proactive navigation. Additionally, the research emphasises the importance of establishing the closest point of approach and time to the closest point of approach parameters within Automatic Radar Plotting Aids. The implementation of these values and their prominent placement in close proximity to radars, coupled with the incorporation of alarms within the Automatic Radar Plotting Aid system, serves to augment the level of awareness among bridge personnel pertaining to the importance of upholding appropriate separation distances to ensure safety. The study underscores the importance of improving the standing orders of the master. This entails the revision of the standing orders to incorporate the insights gained, which includes the proscription of engaging in paperwork or utilising autonomous equipment in the absence of a backup lookout. The study endeavours to enhance safety practises and mitigate the occurrence of comparable incidents in the future by examining and addressing the lessons derived from them. This highlights the significance of striking a harmonious equilibrium between the utilisation of navigation equipment as valuable instruments, while simultaneously upholding the ongoing dependence on human judgement, sustaining situational awareness, and adhering to appropriate navigation protocols.

Analysis of the Improvement of Maritime Safety through Seafarer Skills Training Cooperation between Poltekpel Surabaya and the Main Shipping Office of Tanjung Perak

One effort to enhance seafarer skills is by improving knowledge. Increasing knowledge can be achieved through the implementation of training for ship crews. Through training implementation, it is expected to minimize ship accidents caused by human error, thereby improving maritime safety. This research aims to understand the regulations regarding navigation in the Tanjung Perak harbor area and analyze the role of seafarer skills training in enhancing maritime safety. The method used in this research is qualitative. The data used in this study are primary data obtained directly from respondents through questionnaires and secondary data collected from other sources. The research results show that in the event of an accident within the jurisdiction of the Tanjung Perak Port Master, based on the Minister of Transportation's Decree No. 55 of 2006 concerning Procedures for Ship Accident Inspections, if a ship accident occurs, such as sinking, burning, collision, or grounding, the ship's Master or leader who experiences the accident must report it to the Port Master at the nearest or first port visited. The research also indicates that the implementation of seafarer skills training has an impact on maritime safety. Improving maritime safety can be achieved by participating in self-development activities to enhance the quality of ship personnel (seafarers) for better performance.

Deep reinforcement learning based collision avoidance system for autonomous ships

Autonomous ships is a key to avoid accidents caused by human errors and improve maritime safety. However, unlike the autonomous vehicles counterpart, collision avoidance for autonomous ships faces many challenges due to the hash driving environments, difficult ship control and large stopping distance. In this paper, we investigate a collision avoidance system for autonomous ships under complex encounter scenarios, such as busy ports. In the system various sensors are used to detect objects and perceive the maritime environments. To help the autonomous ships handle the complex and dynamic scenarios that may be encountered, a collision map used to describe the ships encounter scenarios is generated and utilized as the input of a deep reinforcement learning (DRL) model. The DRL model is applied to make collision avoidance and safe driving decisions. New reward functions are proposed to train the DRL model to generate safe ship maneuver actions to reduce collisions and ensure compliance with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Furthermore, a self-adaptive parameters sharing approach is designed for fast convergence and collision avoidance performance of the DRL model, where the parameters of the fully connected layers are shared and the correlation layers are self adapted for the DRL critic and actor networks. Simulation results show that the proposed system has high DRL convergence speed and excellent collision avoidance.

An Analysis of Causes Related to Human Factors in Maritime Accidents

Safety is the most critical job of any Mariner. Recently, the maritime industry has embedded automated systems onboard ships to reduce workload and improve safety. This study explores several recent marine incidents generated by human errors resulting from improper use of technology and the loss of situational awareness. Accidents within three categories of vessels illustrate findings, small passenger vessels, a container ship, and military vessels, using the official accident reports and agency findings. The results indicate that three categories of errors were identified: competence-based, regulatory-based, and perception-based. By sharing this information, Governments, Educators, Mariners, Admiralty Lawyers, and vessel owners can work together to improve maritime safety. This research article proposes academic Maritime Education and Training (MET) strategies and administrative systems to recognise the deficiencies between human factor errors and digital innovation to prevent such accidents from reoccurrence.

Graph-Based Anomaly Detection of Ship Movements Using CCTV Videos

This paper presents a novel machine learning-based approach for detecting abnormal ship movements using CCTV videos. Our method utilizes graph-based algorithms to analyze ship trajectories and identify anomalies, with a focus on enhancing maritime safety and accident prevention. Unlike conventional AIS data-dependent methods, our approach directly detects and visualizes abnormal movements from CCTV videos, particularly in narrow coastal areas. We evaluate the proposed method using real-world CCTV video data and demonstrate its effectiveness in detecting abnormal ship movements, offering promising results in real-world scenarios. The findings of this study have important implications to improve maritime safety and prevent accidents.

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.

Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Therefore, timely detection of fires is essential for quick responses and powerful mitigation. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. Its enhanced feature fusion technique makes it superior to all its predecessors. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Finally, we compared the performance of our method with those of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.