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Efficient ConvNet for Surface Object Recognition

  • Wei Lin
  • Quan Chen
  • Xianzhi Qi
  • Bingli Wu
  • Xue Ke
  • Dezhao Yang
  • Yongzhi Wang
  • Jie MaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)

Abstract

Surface object recognition plays an important role in surface detection system. Comparing with feature-based classifier, deep neural networks have evolved to the state-of-the-art technique for object recognition in complex background. However, excessive memory requirements, expensive computational costs and overmuch energy consumption make it difficult to deploy neural networks on embedded platform such as the environment perception module of the Unmanned surface vessel (USV). In this paper, we propose a dynamic-selecting criterion approach to prune a trained Yolo-v2 model to deal with these drawbacks caused by redundant parameters in network and we can reduce inference costs for Yolo-v2 by up to 65% on it while regaining close to the original performance by retraining the network. Moreover, we introduce a surface object dataset for surface detection system.

Keywords

Surface object recognition Model compression Deep learning 

Notes

Acknowledgments

This work was supported by the Shanghai Aerospace Science and Technology Innovation Program under Grant sast2016063.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wei Lin
    • 1
  • Quan Chen
    • 1
  • Xianzhi Qi
    • 1
  • Bingli Wu
    • 1
  • Xue Ke
    • 1
  • Dezhao Yang
    • 2
  • Yongzhi Wang
    • 2
  • Jie Ma
    • 1
    Email author
  1. 1.National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Shanghai Radio Equipment Research InstituteShanghaiChina

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