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Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework

Published online by Cambridge University Press:  09 July 2021

Xinqiang Chen Open the ORCID record for Xinqiang Chen [Opens in a new window] ,
Jun Ling ,
Shengzheng Wang ,
Yongsheng Yang ,
Lijuan Luo  and
Ying Yan
Xinqiang Chen
Affiliation:
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, PR China
Jun Ling
Affiliation:
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, PR China
Shengzheng Wang
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, PR China
Yongsheng Yang
Affiliation:
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, PR China
Lijuan Luo*
Affiliation:
AI and Data Science Application Center, School of Business and Management, Shanghai International Studies University, Shanghai, China
Ying Yan
Affiliation:
College of Transportation, Chang'an University, Xi'an, China
*
*Corresponding author. E-mail: luolijuan@shisu.edu.cn
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Abstract

Coastal surveillance video helps officials to obtain on-site visual information on maritime traffic situations, which benefits building up the maritime transportation detection infrastructure. The previous ship detection methods focused on detecting distant small ships in maritime videos, with less attention paid to the task of ship detection from coastal surveillance video. To address this challenge, a novel framework is proposed to detect ships from coastal maritime images in three typical traffic situations in three consecutive steps. First the Canny detector is introduced to determine the potential ship edges in each maritime frame. Then, the self-adaptive Gaussian descriptor is employed to accurately rule out noisy edges. Finally, the morphology operator is developed to link the detected separated edges to connected ship contours. The model's performance is tested under three typical maritime traffic situations. The experimental results show that the proposed ship detector achieved satisfactory performance (in terms of precision, accuracy and time cost) compared with other state-of-the-art algorithms. The findings of the study offer the potential of providing real-time visual traffic information to maritime regulators, which is crucial for the development of intelligent maritime transportation.

Keywords

ship detectionensemble Canny-Gaussian-morphology frameworkcoastal surveillance videosmart ship
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 6 , November 2021 , pp. 1252 - 1266
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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