Abstract
The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.
Highlights
70% of the earth’s surface is covered by oceans, inland rivers, and lakes.Increasingly fierce and complicated maritime rights disputes between countries have made integrated maritime surveillance, rights protection, and law enforcement more difficult and arduous and have necessitated improvements in omnidirectional observation and situational awareness in specific areas of the ocean
If the similarity is greater than some threshold value, the target vessel is judged to be illegal for tampering with automated identification systems (AIS) information
Visual sensors are commonly used in addition to navigational radar and AIS to directly detect or identify obstacles or target vessels on the water surface
Summary
70% of the earth’s surface is covered by oceans, inland rivers, and lakes. Increasingly fierce and complicated maritime rights disputes between countries have made integrated maritime surveillance, rights protection, and law enforcement more difficult and arduous and have necessitated improvements in omnidirectional observation and situational awareness in specific areas of the ocean. The rapid development of intelligent shipping requires improved computerized visual perception of the ocean surface. Current visual perception, which uses deep learning, is inadequate for control of an autonomous surface vessel (ASV) and associated marine surveillance. We used the discriminative model in this study It is widely used in visual recognition in maritime situations and learns the posterior probability distribution of visible data. Research into visual perception in the marine environment provides data that can be mined for purposes of ship course planning and intelligent collision avoidance decision-making. It has significant practical value both for improving monitoring of the marine environment from onshore and for improving automated ASV navigation
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