Research on detection mechanism of vessel abnormal behavior based on immune genetic spectral clustering algorithm. (15th December 2022) - Liu, Hongdan; Liu, Yan; Li, Bing; Qi, Zhigang
Abstract: Maritime transport accounts for over 90% of global trade, and maritime safety has been confirmed as a vital issue of maritime transport. Accordingly, vessel traffic service system (VTS) is capable of assisting the maritime department to complete real-time supervision of a certain water area and increasing the efficiency of water supervision. However, the VTS system has low effectiveness in vessel anomaly detection and supervision, thus resulting in regulatory blind spot in the VTS system. In this study, a vessel trajectory anomaly detection mechanism is developed using the immune genetic spectral clustering method. Moreover, the contour coefficient of scatter clustering is accurately analyzed, and the clustering center is optimized using the adopted method. On that basis, different abnormal features can be detected, and abnormal detection accuracy can be increased. In this study, the vessel trajectory anomaly detection model is built in accordance with the set sliding window and abnormal threshold parameters of vessel trajectory characteristics. The AIS data of Lianyungang to Qingdao port from March 2021 are selected for experimental analysis. The results suggest that the proposed method outperforms the conventional method in the detection accuracy and the false alarm rate, it facilitates the intelligent and automatic management of vessels by the VTS system. Highlights: The method of detecting abnormal ship trajectories based on optimized spectral clustering was proposed in this paper. The improved Hausdorff distance and cosine distance superposition algorithm was proposed to measure the similarity of ship features. Based on determined the number of clustering using CHI's and silhouette coefficient. dual analysis. The immune genetic algorithm, which solved the problem that the spectral clustering algorithm is easy to fall into the local optimal solution. The model was Established by setting the abnormal threshold of the trajectory feature attribute and combining with the sliding window algorithm.