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Ship speed extraction (SSE) in busy water based on an automatic identification system (AIS) has received widespread attention due to its high efficiency. However, this method is usually degraded by data delay and loss. In this paper, we propose a novel SSE method based on machine vision and unmanned aerial vehicle (UAV) video to measure ship speed and repair AIS data in busy water. Our framework integrates YOLO v4 into simple online and real-time tracking with a deep association metric (Deep SORT) to detect and track ship targets. Specifically, we design a tracking buffer to promote the insensitivity of the tracker under uncertain environmental changes and filter the tracker for measuring speed. Furthermore, a motion vector computation term is proposed based on sparse optical flow and a box diagram. After using camera calibration to convert the motion vector to displacement in the real world, the SSE results are obtained. Exhaustive experiments are conducted on various scenarios, i.e., illumination and ship distribution, from our ship’s remote dataset. The results verify that the proposed framework performs excellently, with an average speed measurement accuracy above 95% in complex waters, and the AIS data repair effect is remarkable. Moreover, it is also proven that machine vision technology can assist AIS information in supervising complex waters and ensuring navigation safety, which paves the way for extracting ship traffic flow information.
Zhao et al. (Sun,) studied this question.