Abstract: Ship wake is an important military target, while the ocean background data in satellite images is huge, and ship wake targets are few. How to quickly and accurately find targets in large amounts of data is a valuable research topic. The ship wake target has a different detection method from the general target, as it has the characteristics of uniform background and prominent line characteristics. In this paper, we design a ship wake detection method which can quickly screen out easily detected samples by using traditional algorithms including Gaussian filtering, Canny edge detection and probabilistic Hough transform. We used typical representative YOLOv5 and Faster-R-CNN target detection frameworks to train single-stage and two-stage target detection models. In the process of integrating traditional algorithms into ship wake detection, we have built a ship wake target detection system that combines traditional algorithms with neural network methods. The experiment shows that the improved traditional wake detection algorithm can effectively improve the efficiency of ship wake detection system in target detection task.
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