Marine Object Segmentation and Tracking by Learning Marine Radar Images for Autonomous Surface Vehicles
Hanguen Kim, Donghoon Kim, Seung‐Mok Lee
Abstract
This article proposes a radar image segmentation and tracking method by learning radar images for autonomous surface vehicles. To identify marine objects from radar images, we propose a deep neural network named the dual path squeeze and excitation network (DPSE-Net). By learning the radar images, the proposed DPSE-Net is designed to segment every pixel of the radar images into four classes: marine objects, land, noise, and background. The proposed DPSE-Net shows the best performance in radar image segmentation while operating in real-time, compared to state-of-the-art real-time image segmentation network models. In addition, we design a real-time moving object tracking algorithm for estimating the position and velocity of marine objects based on deep simple online real-time tracking with a deep association metric (DeepSORT), a widely used tracking algorithm. The existing DeepSORT algorithm uses the intersection over union (IoU) metric and a deep appearance descriptor for data association, but since they are not suitable for radar images, successive tracking is difficult. To solve this problem, a new data association metric suitable for radar images is proposed. The field tests in ocean environments confirm that the proposed method performs better in marine object segmentation and tracking.