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Ship pollution emissions have attracted increasing attention in the maritime field due to the massive growth of maritime traffic activities. It is important to identify the ship emissions (SEs) magnitude and corresponding spatial and temporal distributions for the purposes of developing appropriate strategies to mitigate environment pollution. The aim of this study was to estimate ship pollution emissions with various typical merchant ship types under different sailing conditions. We estimated the emission variation with a ship traffic emission assessment model (STEAM2), and then the ship pollution emission distribution was further visualized using ArcGIS. We collected data from the automatic identification system (AIS) for ships in New York Harbor and further analyzed the spatiotemporal distribution of pollutant emissions from ships. The experimental results demonstrate that the ship pollutant emission volume in the New York Harbor area in 2022 was 3340 t, while the pollution in terms of CO, SO2, CXHX, PM10, NOX, and PM2.5 was 136, 1421, 66, 185, 1384, and 148 t, respectively. The overall SEs from container ships, passenger ships, and tankers account for a large amount of pollution discharge. The pollutant emissions of container ships are significantly greater than that of their counterparts. Moreover, the spatiotemporal distributions of ship pollutant discharge can vary significantly among different ship types and sailing conditions. [ABSTRACT FROM AUTHOR]

Global greenhouse gas emission attracts significant attentions across varied communities, and carbon emission (CE) reduction has become hot topic in the maritime field considering that appropriately 3% CE come from the field. The prerequisite for fulfilling the task is to accurately quantify the ship CE. To achieve the aim, the study utilizes indicators, such as carbon dioxide (CO 2) emission, CO 2 index, fuel consumption, energy efficiency operational indicator (EEOI), fleet energy efficiency management index (FEEMI), to analyze ship energy consumption. We employ ship voyage data from container, oil tanker, bulk carrier and liquefied natural gas (LNG) carrier to evaluate ship energy consumption. We have testified EEOI variation tendency under different ship cargo loading volume states (i.e., full/partial load) and speed deceleration scenario. Moreover, the FEEMI indicator is used to determine energy efficiency for different ship fleets (container ship fleet, oil tanker fleet, bulk carrier fleet, LNG fleet). Experimental results suggest that EEOI is proportional to ship energy consumption when the sailing distance and cargo volume are constant. The ship EEOI indicator calculated in full-loaded status is obviously smaller than the counterpart under partial-load status. The fleet energy consumption efficiency shows a slight increase (at least 1%) due to release of ship energy efficiency management plan. The research findings can help maritime policy-makers provide more reasonable regulations for the purpose of ship energy consumption enhancement. • Five indicators: CO 2 emissions, CO 2 index, fuel consumption, EEOI and FEEMI are selected to evaluate ship energy efficiency and environmental management. • EEOI is evaluated together with the CO 2 emission to analyze the variation of EEOI via spatial-temporal maritime data. • By quantitative analysis and designing different scenarios, the key factors affecting the change of EEOI are studied. [ABSTRACT FROM AUTHOR]

Ship detention serves as an obligatory but efficient manner in port state control (PSC) inspection, and accurate ship detention prediction provides early warning information for maritime traffic participants. Previous studies mainly focused on exploiting the relationship between ship factors (i.e., ship age and ship type) and PSC inspection reports. Less attention was paid to identify and predict the correlation between ship fatal deficiency and ship detention event. To address the issue, we propose a novel framework to identify crucial ship deficiency types with an optimized analytic hierarchy process (AHP) model. Then, the Naïve Bayes model is introduced to predict the ship detention probability considering weights of the identified crucial ship deficiency types. Finally, we evaluate our proposed model performance on the empirical PSC inspection dataset. The research findings can help PSC officials easily determine main ship deficiencies, and thus, less time cost is required for implementing the PSC inspection procedure. In that manner, the PSC officials can quickly make ship detention decision and thus enhance maritime traffic safety. [ABSTRACT FROM AUTHOR]

Most previous research has handled the task of ship type recognition by exploring hand-craft ship features, which may fail to distinguish ships with similar visual appearances. This situation motivates us to propose a novel deep learning based ship type recognition framework which we have named coarse-to-fine cascaded convolution neural network (CFCCNN). First, the proposed CFCCNN framework formats the input training ship images and data, and provides trainable input data for the hidden layers of the CFCCNN. Second, the coarse and fine steps are run in a nesting manner to explore discriminative features for different ship types. More specifically, the coarse step is trained in a similar manner to the traditional convolution neural network, while the fine step introduces regularisation mechanisms to extract more intrinsic ship features, and fine tunes parameter settings to obtain better recognition performance. Finally, we evaluate the performance of the CFCCNN model for recognising the most common types of merchant ship (oil tanker, container, LNG tanker, chemical carrier, general cargo, bulk carrier, etc.). The experimental results show that the proposed framework obtains better recognition performance than the conventional methods of ship type recognition. [ABSTRACT FROM AUTHOR]

Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety. [ABSTRACT FROM AUTHOR]

Video-based detection infrastructure is crucial for promoting connected and autonomous shipping (CAS) development, which provides critical on-site traffic data for maritime participants. Ship behavior analysis, one of the fundamental tasks for fulfilling smart video-based detection infrastructure, has become an active topic in the CAS community. Previous studies focused on ship behavior analysis by exploring spatial-temporal information from automatic identification system (AIS) data, and less attention was paid to maritime surveillance videos. To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. Second, the proposed framework generated many bounding boxes (i.e., potential ship positions) based on the object confidence level. Third, we suppressed the background bounding box interferences, and determined ship detection results with intersection over union (IOU) criterion, and thus obtained ship positions in each ship image. Fourth, we analyzed spatial-temporal ship behavior in consecutive maritime images based on kinematic ship information. The experimental results have shown that ships are accurately detected (i.e., both of the average recall and precision rate were higher than 90%) and the historical ship behaviors are successfully recognized. The proposed framework can be adaptively deployed in the connected and autonomous vehicle detection system in the automated terminal for the purpose of exploring the coupled interactions between traffic flow variation and heterogeneous detection infrastructures, and thus enhance terminal traffic network capacity and safety. [ABSTRACT FROM AUTHOR]

Ship tracking from maritime visual sensing data (namely maritime surveillance videos) provides various kinematic maritime traffic information, which significantly benefits remote maritime traffic controlling and management, off-site law enforcement, etc. But, it is difficult to extract distinct ship visual features when the target ship is sheltered by the neighboring ship in the maritime images (or the images are shot in low visibility condition). To address the difficulty, we proposed a hybrid ship tracking framework via the help of kernelized correlation filter (KCF) and anomaly cleaning models (including curve fitting method and Kalman filter). First, we employed the KCF model to obtain raw ship trajectories in consecutive maritime images. Second, our ship tracker is accurately initialized (to be specific, ground truth ship position in the first frame is employed to initialize the ship tracker). Third, the Kalman filter is introduced to suppress the trivial ship position oscillations in the raw ship trajectories. We verified the proposed framework performance on the four typical maritime scenarios. The experimental results indicate that the proposed ship tracker showed more accurate ship tracking results compared to other four popular ship trackers in terms of average root mean square error (RMSE), mean absolute deviation (MAD) and mean square error (MSE). The research findings can help maritime traffic participants obtain visual on-spot maritime kinematic information, and thus further enhance maritime traffic safety. [ABSTRACT FROM AUTHOR]

Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker's performance on typical maritime traffic scenarios through four maritime surveillance videos. [ABSTRACT FROM AUTHOR]

Accurate identification and segmentation of moving ships to ensure maritime traffic safety has become an important task in maritime intelligent transportation system. With the development of artificial intelligence, maritime surveillance system based on computer vision has been widely studied. However, there are still some problems when applied to the actual maritime scene. For example, the degradation of visible image quality caused by rain, haze, and low illumination leads to a significant reduction in segmentation performance. To improve the performance of ship identification and segmentation under adverse weather conditions, we propose SwinSeg, a hybrid network combining Swin Transformer and lightweight multi-layer perceptron (MLP). To address the lack of suitable open-source datasets in the community, we have collected and labeled a semantic segmentation dataset of marine ships, named SeaShipsSeg. It consists of 1200 visible marine ship images and covers six common ship types (bulk cargo carrier, container ship, fishing boat, general cargo ship, ore carrier, and passenger ship). In addition, synthetic degraded images are added to the dataset to increase its diversity and improve the generalization ability of the network. The experimental results show that the performance of our method is significantly better than the state-of-the-art (SOTA) methods in terms of segmentation accuracy, robustness, and efficiency under different weather conditions. The dataset is available at https://github.com/GrimreaperZ-creator/SeaShipsSeg. [Display omitted] • A semantic segmentation dataset for marine ship is constructed. • A hybrid network combining Swin Transformer and lightweight MLP is designed. • In adverse weather conditions, the ship can be robustly identified and segmented. [ABSTRACT FROM AUTHOR]