Fishing Vessel Type Recognition Based on Semantic Feature Vector

Junfeng Yuan, Qianqian Zhang, Jilin Zhang, Youhuizi Li, Zhen Liu, Meiting Xue and Yan Zeng
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Junfeng Yuan: Hangzhou Dianzi University, China
Qianqian Zhang: Hangzhou Dianzi University, China
Jilin Zhang: School of Computer Science and Technology, Hangzhou Dianzi University, China
Youhuizi Li: Hangzhou Dianzi University, China
Zhen Liu: Hangzhou Dianzi University, China
Meiting Xue: Hangzhou Dianzi University, China
Yan Zeng: Hangzhou Dianzi University, China

International Journal of Data Warehousing and Mining (IJDWM), 2024, vol. 20, issue 1, 1-18

Abstract: Identifying fishing vessel types with artificial intelligence has become a key technology in marine resource management. However, classical feature modeling lacks the ability to express time series features, and the feature extraction is insufficient. Hence, this work focuses on the identification of trawlers, gillnetters, and purse seiners based on semantic feature vectors. First, we extract trajectories from massive and complex historical Vessel Monitoring System data that contain a large amount of dirty data and then extract the semantic features of fishing vessel trajectories. Finally, we input the semantic feature vectors into the LightGBM classification model for classification of fishing vessel types. In this experiment, the F1 measure of our proposed method on the East China Sea fishing vessel dataset reached 96.25, which was 6.82% higher than that of the classical feature-modeling method based on fishing vessel trajectories. Experiments show that this method is accurate and effective for the classification of fishing vessels.

Date: 2024
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