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Volume 28 Issue 6
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2020  >  28(6): 597-603
ZHANG Qian, ZHANG You-mei, LI Xiao-lei, SONG Ran, ZHANG Wei. Maritime Object Detection Method Based on Self-Supervised Representation Learning[J]. Journal of Unmanned Undersea Systems, 2020, 28(6): 597-603. doi: 10.11993/j.issn.2096-3920.2020.06.002
Citation: ZHANG Qian, ZHANG You-mei, LI Xiao-lei, SONG Ran, ZHANG Wei. Maritime Object Detection Method Based on Self-Supervised Representation Learning[J]. Journal of Unmanned Undersea Systems, 2020, 28(6): 597-603. doi: 10.11993/j.issn.2096-3920.2020.06.002
shu

Maritime Object Detection Method Based on Self-Supervised Representation Learning

doi: 10.11993/j.issn.2096-3920.2020.06.002

  • School of Control Science and Engineering, Shandong University, Jinan 250061, China 2. School of Mathematics and Statistics, Qilu University of Technology Shandong Academy of Sciences, Jinan 250353, China

  • Received Date: 2020-09-07
  • Rev Recd Date: 2020-11-12
  • Publish Date: 2020-12-31
    Abstract
  • To improve the perception and monitoring ability of marine unmanned equipment, boosting the performance of maritime object detection is critical. However, complex sea environments and limited sensors make it difficult to collect high-quality samples for a large-scale maritime dataset. This results in a dearth of large-scale sea surface target datasets, which in turn hampers the development of maritime object detection based on deep earning. To address this problem, this study introduces self-supervised representation learning into the field of maritime object detection. Specifically, a momentum-contrast based algorithm is proposed to conduct representation learning of ships, where the characteristics of ship targets are learned from large-scale unlabeled maritime data. This provides prior knowledge for subsequent maritime object detection based on Faster R-CNN. Experimental results show that with the aid of model pre-training on a large-scale unlabeled dataset in a self-supervised manner, the proposed maritime object detection method through self-supervised representation learning has a performance comparable with those that employ supervised model pre-training. The proposed method can thus overcome the limitations caused by an inadequate number of labeled maritime samples.

     

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