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Small-scale moving target detection in aerial image by deep inverse reinforcement learning

  • Wei SunEmail author
  • Dashuai Yan
  • Jie Huang
  • Changhao Sun
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Abstract

It proposes a deep inverse reinforcement learning method for slow and weak moving targets detection in aerial video. Differential gray images of adjacent frames are used as the network model input, and the feature network layer extracts the candidate moving target regions through the multi-layer convolution. The candidate target information is used as the initial layer of the policy network. The expert trajectory is used to adjust and optimize the feature convolution network model and the policy fully connected network model to realize the training the reward return function and the expert policy. In the stage of autonomous improvement policy, the policy model is re-optimized by unmarked aerial video, and deep inverse reinforcement learning and nonlinear policy network are used to make decision on moving target position and size information. The target size of the multi-group aerial video test set is 10 * 10 pixels. Experimental results show that the proposed algorithm has the advantage of the nonlinear policy of the neural network compared with the traditional moving target detection algorithm, and the detection result is more accurate. At the same time, compared with the traditional marginal programming (MMP) method and the structured classification based (SCIRL) method, the proposed algorithm shows obvious advantages in the accuracy of aerial video moving target detection.

Keywords

Aerial image Deep inverse reinforcement Small-scale target detection 

Notes

Acknowledgements

We would like to thank the anonymous reviewers and the associate editor for their valuable comments and suggestions to improve the quality of the manuscript. This work was supported by National Nature Science Foundation of China (NSFC) under Grants 61671356, 61703403, 61601352.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anChina
  2. 2.Qian Xuesen Laboratory of Space TechnologyChina Academy of Space TechnologyBeijingChina

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