Image small target detection based on deep learning with SNR controlled sample generation

Ming Liu*, Hao yuan Du, Yue jin Zhao, Li quan Dong, Mei Hui

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

54 Citations (Scopus)

Abstract

A small target detection method based on deep learning is proposed. First, random background parts are sampled from some cloud-sky images. Then, random generated target spots are added to the backgrounds with controlled signal to background noise ratio (SNR) to generate target samples. Then training and testing results show that the performance of deep nets is superior to tradition small target detection techniques and the selection of sampling SNRhas an important effect on nets training performances. SNR = 1 is a good selection for deep nets training, not onlyfor small target detection,but also for other applications.

Original languageEnglish
Title of host publicationCurrent Trends in Computer Science and Mechanical Automation Vol.1
Subtitle of host publicationSelected Papers from CSMA2016
Publisherde Gruyter
Pages211-220
Number of pages10
ISBN (Electronic)9783110584974
ISBN (Print)9783110584967
Publication statusPublished - 9 Jan 2018

Keywords

  • Deep learning
  • Nerual Network
  • SNR control
  • Small target detection

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