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access icon free Specific ship detection for electronic reconnaissance data based on clustering and NNs

  • Author(s): Wenbin Chen 1, 2, 3 ; Guangluan Xu 2, 3 ; Wenjuan Ren 2, 3 ; Tinglei Huang 2, 3
    • View affiliations
  • Source: Volume 12, Issue 8, August 2018, p. 868 – 872
    DOI:  10.1049/iet-rsn.2018.0003 , Print ISSN 1751-8784, Online ISSN 1751-8792
© The Institution of Engineering and Technology
Received 07/01/2018, Accepted 13/04/2018, Revised 10/04/2018, Published 16/04/2018

Ship detection is an important issue in both civil and military fields. There are many researches about ship detection in wide sea areas and inshore areas. Most of these researches use the image dataset. In this research, a novel framework for specific ship detection using the electronic reconnaissance dataset is proposed. In the new framework, the clustering methods based on time and trajectory are used to process the dataset. Then, several machine-learning methods are used to build detection models. The long short-term memory models are used to extract time-series features and the carefully designed one-dimensional (1D) convolution neural networks (CNNs) are introduced to extract local features. Experimental results on a large electronic reconnaissance dataset collected from real scenario show the model based on 1D CNN gets better performance than classic detection models and the authors’ system achieves a good detection accuracy of 92.5%. Above all, this research is a very valuable exploration for the detection of specific ship based on electronic reconnaissance dataset.

Inspec keywords: object detection; feature extraction; learning (artificial intelligence); neural nets; time series

Other keywords: long short-term memory models; machine-learning methods; one-dimensional convolution neural networks; clustering methods; ship detection; time-series features; electronic reconnaissance data

Subjects: Other topics in statistics; Knowledge engineering techniques; Other topics in statistics; Optical, image and video signal processing; Computer vision and image processing techniques; Neural computing techniques

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