Recognition and Tracking of Aerial Targets Using Convolutional Neural Network

Document Type : Original Article

Author

Assistant Professor, Khatam Al Anbia Air Defense University, Tehran, Iran

Abstract

Automatic recognition and tracking systems of aerial targets are of particular importance in the battle field. These types of systems use visual sensors, have the ability to be installed on various military systems, and are suitable for discovering and tracking low-altitude targets. In this manuscript, a convolutional neural network was designed to recognize the type of aerial targets (cargo, aerobatics, fighter and missile) and then target tracking using a pre-trained network (GoogLeNet) and transfer learning in the form of a region with convolutional neural network was done. The recognition accuracy of aerial targets in the test data set is 96.3%. On the other hand, the overlap value between the actual and predicted bounding box of target in the test data set for cargo and aerobatics plane, fighter and missile is 0.61, 0.66, 0.64 and 0.51, respectively, which shows the desirable accuracy of the developed model for targets tracking in consecutive frames.

Keywords

Smiley face

References

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Radar
Volume 10, Issue 2 - Serial Number 28
Number 28, Autumn and Winter Quarterly
January 2023

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History

  • Receive Date: 08 September 2022
  • Revise Date: 17 December 2022
  • Accept Date: 01 January 2023
  • Publish Date: 21 January 2023

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