Ship Collision Avoidance Navigation Signal Recognition via Vision Sensing and Machine Forecasting

Ship collision avoidance (SCA) is an important technique in the field of decision-making in marine navigation. Although some promising solutions have been developed recently, there is still the lack of low-cost and reliable sensing equipment. Inspired by the low-cost of camera sensors and the succes...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-11, Vol.24 (11), p.1-13
Main Authors: Bi, Qilin, Wang, Miaohui, Huang, Yijing, Lai, Minling, Liu, Zhijun, Bi, Xiuying
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Collision avoidance
Field of view
Image resolution
Imaging
light recognition
Low cost
Machine learning
Marine vehicles
Navigation
Optical imaging
Optical sensors
ship recognition
Surface navigation
vision sensing
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Summary:Ship collision avoidance (SCA) is an important technique in the field of decision-making in marine navigation. Although some promising solutions have been developed recently, there is still the lack of low-cost and reliable sensing equipment. Inspired by the low-cost of camera sensors and the success of machine learning, this paper designs a vision-based method to recognize ships and their micro-features for SCA navigation planning. Firstly, we develop a vision-based bearing, distance and velocity model based on a wide-field optical imaging system. Secondly, optical information is used to construct the micro-characteristic imaging model of ship navigation signals. Thirdly, we have solved the problem between a large field-of-view (FOV) and high-resolution imaging in vision-based marine navigation. Finally, an improved Adaboost algorithm is designed for the intelligent recognition of an open-sea target (ship types and light patterns). The proposed method has been verified by extensive experiments in a practical environment, and the results show that it can effectively and efficiently identify the navigation signal of a target ship.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3287709