IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i3p1550147720912959.html
   My bibliography  Save this article

Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network

Author

Listed:
  • Zhijing Xu
  • Yuhao Huo
  • Kun Liu
  • Sidong Liu

Abstract

Deep learning algorithms have been increasingly used in ship image detection and classification. To improve the ship detection and classification in photoelectric images, an improved recurrent attention convolutional neural network is proposed. The proposed network has a multi-scale architecture and consists of three cascading sub-networks, each with a VGG19 network for image feature extraction and an attention proposal network for locating feature area. A scale-dependent pooling algorithm is designed to select an appropriate convolution in the VGG19 network for classification, and a multi-feature mechanism is introduced in attention proposal network to describe the feature regions. The VGG19 and attention proposal network are cross-trained to accelerate convergence and to improve detection accuracy. The proposed method is trained and validated on a self-built ship database and effectively improve the detection accuracy to 86.7% outperforming the baseline VGG19 and recurrent attention convolutional neural network methods.

Suggested Citation

  • Zhijing Xu & Yuhao Huo & Kun Liu & Sidong Liu, 2020. "Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network," International Journal of Distributed Sensor Networks, , vol. 16(3), pages 15501477209, March.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:3:p:1550147720912959
    DOI: 10.1177/1550147720912959
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720912959
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147720912959?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:intdis:v:16:y:2020:i:3:p:1550147720912959. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.