Wuhan University

Shanghai Maritime University

Huafeng Wu

Yang Sun

Yongsheng Yang

Xinqiang Chen

Yu Wang

Bing Han

Jiangfeng Xian

Zhongdai Wu

Hao Wu

Jing Chen

Bing Wu

Qiuying Wang

Zichuang Wang

Chenxin Wei

Chaofeng Li

Zewei Yu

Jansen Zhao

Junjun Li

Wei Liu

Huixing Chen

Bowei Xu

Daofang Chang

Junjie Fu

Qiannan Zhang

Disclosed is a ship image track tracking and prediction method based on ship heading recognition, which includes the following steps: obtaining a ship image data set, preprocessing the data set to obtain a preprocessed data set; inputting the preprocessed data set into the rotating ship detection network for training, obtaining the trained rotating ship detection network, collecting the ship navigation video, and inputting the ship navigation video into the trained rotating ship detection network to obtain the ship detection result; inputting the ship detection result into the rotating ship tracking network and tracking the target ship to obtain the historical trajectory and the heading information of the target ship; inputting the historical trajectory and ship heading information of the target ship into the ship trajectory and ship heading prediction network, and predicting the navigation trajectory and ship heading at sea.

Ship image trajectory tracking and prediction method based on ship heading recognition

The present invention proposes an ensemble deep learning method for identifying unsafe behaviors of operators in maritime working environment. Firstly, extract features of maritime images with the You Only Look Once (YOLO) V3 model, and then enhance a multi-scale detection capability by introducing a feature pyramid structure. Secondly, obtain instance-level features and time memory features of the operators and devices in the maritime working environment with the Joint Learning of Detection and Embedding (JDE) paradigm. Thirdly, transfer spatial-temporal interaction information into a feature memory pool, and update the time memory features with the asynchronous memory updating algorithm. Finally, identify the interaction between the operators, the devices, and unsafe behaviors with an asynchronous interaction aggregation network. The proposed invention can accurately determine the unsafe behaviors of the operators, and thus provide operation decisions for maritime management relevant activities.

Ensemble deep learning method for identifying unsafe behaviors of operators in maritime working environment

The present invention provides a vessel traffic pattern identification method via data quality control and data compression, and includes the steps of assorting a collection of Automatic Identification System (AIS) data points according to Maritime Mobile Service Identity (MMSI) code; sorting each collection result by time ascending order; deleting duplicated vessel AIS data points considering time stamp, latitude, longitude and vessel speed over ground; segmenting vessel trajectories; obtaining high-quality AIS data with an AIS data anomaly detection; repairing and compressing each vessel trajectory with the Douglas-Peucker algorithm; clustering vessel trajectories with the Quick Bundles algorithm; and identifying a maritime traffic pattern. The invention can efficiently identify vessel traffic patterns and help maritime traffic management departments to accurately identify a traffic situation.

Method for vessel traffic pattern recognition via data quality control and data compression

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Shanghai, China

Huafeng Wu