Ship Wake Detection Based on One-Dimensional Convolutional Neural Network

YANG Changsheng; GOU Wenbo; LIANG Hong

doi:10.11993/j.issn.2096-3920.2022-0052

Volume 31 Issue 6

Dec 2023

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Article Navigation > Journal of Unmanned Undersea Systems > 2023 > 31(6): 839-846

YANG Changsheng, GOU Wenbo, LIANG Hong. Ship Wake Detection Based on One-Dimensional Convolutional Neural Network[J]. Journal of Unmanned Undersea Systems, 2023, 31(6): 839-846. doi: 10.11993/j.issn.2096-3920.2022-0052

Citation:

YANG Changsheng, GOU Wenbo, LIANG Hong. Ship Wake Detection Based on One-Dimensional Convolutional Neural Network[J]. Journal of Unmanned Undersea Systems, 2023, 31(6): 839-846. doi: 10.11993/j.issn.2096-3920.2022-0052

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Ship Wake Detection Based on One-Dimensional Convolutional Neural Network

doi: 10.11993/j.issn.2096-3920.2022-0052

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Received Date: 2022-08-29
Accepted Date: 2022-11-09
Rev Recd Date: 2022-09-22

Available Online: 2023-11-22

Abstract

Abstract

In order to improve the detection accuracy of ship wake, this paper proposed a ship wake detection method based on a one-dimensional convolutional neural network (1DCNN). Firstly, the simulation data set was constructed by using the ship wake scattering echo model. Then the reliability of the scattering echo model was verified by the water tank simulation experiment, and the experimental data set was constructed. Finally, a 1DCNN was built by comprehensively considering the detection accuracy and parameter quantity of different structural models and compared with the traditional detection algorithm (based on a one-class support vector machine and back propagation neural network) on the data set. The simulation results show that compared with the traditional detection algorithm, the 1DCNN proposed in this paper improves the detection accuracy and detection efficiency of ship wake under different signal-to-noise ratios and has good engineering application value.
- ship wake detection,
- one-dimensional convolutional neural network,
- one-class support vector machine,
- back propagation neural network

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References(11)

References

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