Riverside monitoring systems are used for controlling the passage of ships, counting them to prevent overcrowding in a port, or raising an alarm if the ship is unknown or not safe. This type of control and analysis is commonly carried out by many people who supervise CCTV in real time. In this paper, we present an alternative approach to automatic image analysis using a variety of artificial intelligence techniques. Based on collaborative learning, these are punished if they make an incorrect classification. The main advantage is the possibility of continually increasing the amount of knowledge during system operation. However, overtraining is possible, so each time, the best classifier is chosen. Another advantage for practical use is the small database, which allows for the quick and practical implementation of such a system. To verify its effectiveness, this ship classification system was tested on data obtained in a Polish city, Szczecin, as part of a bigger project for classifying inland ships and publicly available databases (for more general ship problems).
Keywords: Convolutional neural network; Image processing; Key-point analysis; Ship classification.
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