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A small fishing vessel recognition method using transfer learning based on laser sensors
Zheng, J.; Cao, J.; Yuan, K.; Liu, Y. (2023). A small fishing vessel recognition method using transfer learning based on laser sensors. NPG Scientific Reports 13(1): 5931. https://dx.doi.org/10.1038/s41598-023-31319-y
In: Scientific Reports (Nature Publishing Group). Nature Publishing Group: London. ISSN 2045-2322; e-ISSN 2045-2322
Peer reviewed article  
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  • Zheng, J.
  • Cao, J.
  • Yuan, K.
  • Liu, Y.
Abstract
    The management of small vessels has always been key to maritime administration. This paper presents a novel method for recognizing small fishing vessels based on laser sensors. Using four types of small fishing vessels as targets, a recognition method for small fishing vessels based on Markov transition field (MTF) time-series images and VGG-16 transfer learning is proposed. In contrast to conventional methods, this study uses polynomial fitting to obtain the contours of a fishing vessel and transforms one-dimensional vessel contours into two-dimensional time-series images using the MTF coding method. The VGG-16 model is used for the recognition process, and migration learning is applied to improve the results. The UCR time-series public dataset is used as a transfer learning dataset for the MTF time-series image encoding. The experiment demonstrates that the proposed method exhibits higher accuracy and performance than 1D-CNN and other general neural network models, and the highest accuracy rate is 98.92%.
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