Research on Ship Wake Detection Method Based on Composite Mode

Hao Xing, Yankai Wang*, Jipeng Han, Jiaqi Bai

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2023 15th International Conference on Computer Research and Development, ICCRD 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages290-297
Number of pages8
ISBN (Electronic)9781665487504
DOIs
Publication statusPublished - 2023
Event15th International Conference on Computer Research and Development, ICCRD 2023 - Virtual, Online, China
Duration: 10 Jan 202312 Jan 2023

Publication series

Name2023 15th International Conference on Computer Research and Development, ICCRD 2023

Conference

Conference15th International Conference on Computer Research and Development, ICCRD 2023
Country/TerritoryChina
CityVirtual, Online
Period10/01/2312/01/23

Keywords

  • Canny edge detection
  • deep learning
  • neural network
  • probabilistic Hough transform
  • ship wake detection

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