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ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

Editor-in-Chief

Associate Editor
Tomasz Neumann
 

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TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@am.gdynia.pl
Ship Recognition and Tracking System for Intelligent Ship Based on Deep Learning Framework
1 Shanghai Maritime University, Shanghai, China
ABSTRACT: Automatically recognizing and tracking dynamic targets on the sea is an important task for intelligent navigation, which is the prerequisite and foundation of the realization of autonomous ships. Nowadays, the radar is a typical perception system which is used to detect targets, but the radar echo cannot depict the target’s shape and appearance, which affects the decision-making ability of the ship collision avoidance. Therefore, visual perception system based on camera video is very useful for further supporting the autonomous ship navigational system. However, ship’s recognition and tracking has been a challenge task in the navigational application field due to the long distance detection and the ship itself motion. An effective and stable approach is required to resolve this problem. In this paper, a novel ship recognition and tracking system is proposed by using the deep learning framework. In this framework, the deep residual network and cross-layer jump connection policy are employed to extract the advanced ship features which help enhance the classification accuracy, thus improves the performance of the object recognition. Experimentally, the superiority of the proposed ship recognition and tracking system was confirmed by comparing it with state of-the-art algorithms on a large number of ship video datasets.
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Citation note:
Liu B., Wang S.Z., Xie Z.X., Zhao J.S., Li M.F.: Ship Recognition and Tracking System for Intelligent Ship Based on Deep Learning Framework. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 13, No. 4, doi:10.12716/1001.13.04.01, pp. 699-705, 2019

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