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A small fishing vessel recognition method using transfer learning based on laser sensors.

Jianli Zheng1, Jianjun Cao1, Kun Yuan2

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Summary

This study introduces a new laser sensor method for identifying small fishing vessels using Markov transition field (MTF) images and VGG-16 transfer learning, achieving 98.92% accuracy.

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Area of Science:

  • Maritime Administration
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Effective management of small vessels is crucial for maritime administration.
  • Existing methods for small fishing vessel recognition have limitations.
  • Developing automated and accurate recognition systems is essential for maritime safety and efficiency.

Purpose of the Study:

  • To propose a novel method for recognizing small fishing vessels using laser sensor data.
  • To leverage Markov transition field (MTF) time-series images and VGG-16 transfer learning for enhanced recognition accuracy.
  • To compare the proposed method against conventional techniques and other neural network models.

Main Methods:

  • Utilized polynomial fitting to extract vessel contours from laser sensor data.
  • Transformed one-dimensional vessel contours into two-dimensional time-series images via MTF coding.
  • Employed the VGG-16 model with transfer learning, using the UCR time-series dataset for training.
  • Compared the proposed MTF-VGG-16 method with 1D-CNN and other general neural network models.

Main Results:

  • The proposed MTF-VGG-16 method achieved a highest accuracy rate of 98.92%.
  • Demonstrated superior performance and accuracy compared to 1D-CNN and other general neural network models.
  • The transfer learning approach significantly improved the recognition results.

Conclusions:

  • The novel method based on MTF time-series images and VGG-16 transfer learning is highly effective for small fishing vessel recognition.
  • This approach offers a significant advancement over traditional methods in terms of accuracy and performance.
  • The findings have strong implications for improving maritime administration and surveillance systems.