Liu, X.; Hu, Y.; Ji, H.; Zhang, M.; Yu, Q. A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area. J. Mar. Sci. Eng.2023, 11, 1259.
Liu, X.; Hu, Y.; Ji, H.; Zhang, M.; Yu, Q. A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area. J. Mar. Sci. Eng. 2023, 11, 1259.
Liu, X.; Hu, Y.; Ji, H.; Zhang, M.; Yu, Q. A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area. J. Mar. Sci. Eng.2023, 11, 1259.
Liu, X.; Hu, Y.; Ji, H.; Zhang, M.; Yu, Q. A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area. J. Mar. Sci. Eng. 2023, 11, 1259.
Abstract
Newly build offshore wind farms (OWFs) render a collision risk between ships and installations. The paper proposed a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method is tested in offshore water in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments.
Keywords
traffic safety; offshore wind farms; YOLOv3; stereo vision; deep learning
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
