Ship Kelvin Wake Velocity Inversion Method Based on KelvinPointNet in Polarization Images

Monitoring ships and their motion states is vital for the marine economy. Traditional methods focus on directly detecting ship hulls but face significant challenges with smaller vessels or those with camouflage patterns on their surfaces. In contrast, detecting ship wakes offers a promising alternative. Ship wakes contain essential information about a ship’s heading and velocity, persist for extended periods, and are easier to observe. By examining the characteristics of wakes, we effectively overcome the limitations of direct detection methods. Kelvin wakes, characterized by well-established theoretical models, are frequently employed in this article. In addition, the polarization imaging technology provides advantages such as antiscattering capabilities, reduced glare on the water surface, and enhanced detection of weak targets, making it particularly effective for wake detection applications. This article illustrates the benefits of polarization imaging in analyzing ship wakes, using the polarimetric bidirectional reflection distribution function (pBRDF) as a framework. We model Kelvin wakes and simulate images of wakes for various ship motion states. Furthermore, we capture polarized visible light images of ship wakes, creating a novel dataset. We develop the KelvinPointNet model, based on key point detection of wake skeletons, to extract critical features of Kelvin wakes and facilitate the inversion of ship velocity and heading. Experimental results show that our algorithm achieves a key point detection accuracy of 92.7%. By employing the Kelvin transverse wave inversion method, we attain a velocity inversion error of less than 10% and a heading inversion error of less than 2.5%, underscoring the substantial implications of our research.