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.
Ship Collision Avoidance Navigation Signal Recognition via Vision Sensing and Machine Forecasting
IEEE Transactions on Intelligent Transportation Systems ; 24 , 11 ; 11743-11755
2023-11-01
2784930 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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