Ship recognition and tracking system for intelligent ship based on deep learning framework

• Autorzy: Liu B. , Wang S. Z. , Xie Z. X. , Zhao J. S. , Li M. F.

Identyfikatory

DOI

10.12716/1001.13.04.01

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

intelligent ship; deep learning framework; ship recognition system; ship tracking system; ship recognition and tracking system; intelligent navigation; autonomous ship; maritime safety

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2019
• Tom: Vol. 13. no. 4
• Strony: 699--705
• Opis fizyczny: Bibliogr. 26 poz., rys.

Twórcy

autor

Liu B.

• Shanghai Maritime University, Shanghai, China

autor

Wang S. Z.

• Shanghai Maritime University, Shanghai, China

autor

Xie Z. X.

• Shanghai Maritime University, Shanghai, China

autor

Zhao J. S.

• Shanghai Maritime University, Shanghai, China

autor

Li M. F.

• Shanghai Maritime University, Shanghai, China

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