Classification of Maritime Vessels using Convolutional Neural Networks

Abstract

Due to a steady increase in traffic at sea, the need for support in surveillance task is growing for coast guards and other law enforcement units all over the world. An important cornerstone is a reliable vessel classification, which can be used for detecting criminal activities like illegal, unreported and unregulated fishing or smuggling operations. As many ships are required to transmit their position by using the automatic identification system (AIS), it is possible to generate a large dataset containing information on the world wide traffic. This dataset is used for implementing deep neural networks based on residual neural networks for classifying the most common shiptypes based on their movement patterns and geographical features. This method is able to reach a competitive result. Further, the results show the effectiveness of residual networks in time-series classification.