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.

An Automatic Internal Wave Recognition Algorithm Based on CNN Applicable to an Ocean Data Buoy System

“…Their application has revolutionized the way we tackle various challenges and tasks. With their ability to analyze large amounts of data and extract meaningful features [33,34], CNNs have been extensively applied in ocean engineering, including ocean data analysis, ocean environmental monitoring, marine robotics, and autonomous systems [35][36][37][38][39][40][41][42]. Him et al [35] show that a statistical forecast model employing a CNN approach produces skilled ENSO forecasts for lead times of up to one and a half years.…”

“…Jing Y. et al [38] apply a CNN to construct the mapping relationship between wind data and wave data, which takes an hourglass configuration. Zhou Z. et al [39] proposed a framework for ship speed extraction based on deep learning, taking into consideration the application of ship detection and tracking technology in hazy environments. Lu et al [40] use the CNN-LSTM approach and utilize spatiotemporal information from the CYGNSS observations to establish an innovative model for ocean wind speed inversion.…”

Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance

“…Undoubtedly, the negative impact of low visibility will make it tricky to analyze critical information in the image, which brings difficulty in subsequent tasks [7]. For instance, it has been proven that low visibility will reduce the precision of object detection [8][9][10], image semantic segmentation [11,12], etc. Therefore, an effective and real-time method for low-visibility image enhancement is required in various domains, such as visual navigation [13], maritime management [14], etc.…”