A Ship Tracking and Speed Extraction Framework in Hazy Weather Based on Deep Learning

Abstract

Obtaining ship navigation information from maritime videos can significantly improve maritime supervision efficiency and enable timely safety warnings. Ship detection and tracking are essential technologies for mining video information. However, current research focused on these advanced vision tasks in maritime supervision is not sufficiently comprehensive. Taking into account the application of ship detection and tracking technology, this study proposes a deep learning-based ship speed extraction framework under the haze environment. First, a lightweight convolutional neural network (CNN) is used to remove haze from images. Second, the YOLOv5 algorithm is used to detect ships in dehazed marine images, and a simple online and real-time tracking method with a Deep association metric (Deep SORT) is used to track ships. Then, the ship’s displacement in the images is calculated based on the ship’s trajectory. Finally, the speed of the ships is estimated by calculating the mapping relationship between the image space and real space. Experiments demonstrate that the method proposed in this study effectively reduces haze interference in maritime videos, thereby enhancing the image quality while extracting the ship’s speed. The mean squared error (MSE) for multiple scenes is 0.3 Kn on average. The stable extraction of ship speed from the video achieved in this study holds significant value in further ensuring the safety of ship navigation.