Automatic ship classification for a riverside monitoring system using a cascade of artificial intelligence techniques including penalties and rewards. (February 2022)

• Record Type: Journal Article
• Authors: Połap, Dawid; Włodarczyk-Sielicka, Marta; Wawrzyniak, Natalia
• Is Part Of: ISA transactions. Volume 121(2022)
• Page Start: 232
• Page End: 239
• Publication Date: 2022-02
• Subjects: Ship classification -- Image processing -- Convolutional neural network -- Key-point analysis; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.04.003
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: 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). Highlights: A cascade of classifiers with a priority of classification for nonconventional ships. A model of classification improved by forcing retraining based on new samples. Experimental data based on a case study on the river in a Polish city Szczecin.