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Infrared Dim and Small Target Detection Based on Denoising Autoencoder Network

  • Manshu ShiEmail author
  • Huan Wang
Article
  • 24 Downloads

Abstract

The method of infrared small target detection is a crucial technology for infrared early-warning tasks, infrared imaging guidance, and large field of view target monitoring, and it is very important for certain early-warning tasks. In this paper, we propose an end-to-end infrared small target detection model (called CDAE) based on denoising autoencoder network and convolutional neural network, which treats small targets as “noise” in infrared images and transforms small target detection tasks into denoising problems. In addition, we use the perceptual loss to solve the problem of background texture feature loss in the encoding process, and propose the structural loss to make up for the perceptual loss defect in which small targets appear. We compare ten methods on six sequences and one single-frame dataset. Experimental results show that our method obtains the highest SCRG value on four sequences and the highest BSF value on six sequences. From the ROC curve, we can see that our method achieves the best results in all test sets.

Keywords

Small target detection Infrared images Denoising autoencoder End-to-end deep model Image gradient 

Notes

Acknowledgments

The paper is supported by National Natural Science Foundation of China (61703209,61773215).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Nanjing University of Science and TechnologyNanjingChina

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