Chun Yang

Ship dimensions are an important component of static AIS information, and are a key factor in identifying the risks of ship collisions. We describe a method of extracting and correcting ship contour information using inland waterway surveillance video combined with AIS information that does not depend on ship dimension data. A lightweight object detection model was used to determine the ship’s position in an image. Dynamic AIS information was included to produce multigroup control points, solve the optimal homography matrix, and create a transformation model to map image coordinates onto water surface coordinates. A semantic segmentation DeepLabV3+ model was used to determine ship contours from the images, and the actual dimensions of the ship contours were calculated using homography matrix transformation. The mAP of the proposed object detection model and the MIoU of the semantic segmentation model were 86.73% and 91.07%, respectively. The calculation error of the ship length and width were 5.8% and 7.4%, respectively. These statistics indicate that the proposed method rapidly and accurately detected target ships in images, and that the model estimated ship dimensions within a reasonable range.

Outliers of ship trajectory from the Automatic Identification System (AIS) onboard a ship will affect the accuracy of maritime situation awareness, especially for a regular ship trajectory mixed with a spoofing ship, which has an unauthorized Maritime Mobile Service Identification code (MMSI) owned by a regular ship. As has been referred to in the literature, the trajectory of these spoofing ships would simply be removed, and more AIS data would be lost. The pre-processing of AIS data should aim to retain more information, which is more helpful in maritime situation awareness for the Maritime Safety Administration (MSA). Through trajectory feature mining, it has been found that there are obvious differences between the trajectory of a regular ship and that of a regular ship mixed with a spoofing ship, such as in terms of speed and distance between adjacent trajectory points. However, there can be a long update time interval in the results of severe missing trajectories of a ship, bringing challenges in terms of the identification of spoofing ships. In order to accurately divide the regular ship trajectory and spoofing ship trajectory, combined with trajectory segmentation by the update time interval threshold, the isolation forest was adopted in this work to train the labeled trajectory point of a regular ship mixed with a spoofing ship. The experimental results show that the average accuracy of the identification of spoofing ships using isolation forest is 88.4%, 91%, 93.1%, and 93.3%, corresponding to different trajectory segmentation by update time intervals (5 h, 10 h, 15 h, and 20 h). The research conducted in this study can almost eliminate the outliers of ship trajectory, and it also provides help for maritime situation awareness for the MSA.

Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.