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).

河滨监控系统用于控制船只的通行、对船只进行计数以防止港口过度拥挤，或者在船只未知或不安全时发出警报。这种控制和分析通常由许多实时监督闭路电视的人进行。在本文中，我们提出了一种使用各种人工智能技术进行自动图像分析的替代方法。基于协作学习，如果他们做出不正确的分类，就会受到惩罚。主要优点是可以在系统运行期间不断增加知识量。但是，过度训练是可能的，因此每次都会选择最佳分类器。实际使用的另一个优点是小型数据库，它允许快速实用地实施这种系统。