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Jahedsaravani, A. (2023). Recognition and Tracking of Aerial Targets Using Convolutional Neural Network. Electronic and Cyber Defense, 10(2), 1-14.
Ali Jahedsaravani. "Recognition and Tracking of Aerial Targets Using Convolutional Neural Network". Electronic and Cyber Defense, 10, 2, 2023, 1-14.
Jahedsaravani, A. (2023). 'Recognition and Tracking of Aerial Targets Using Convolutional Neural Network', Electronic and Cyber Defense, 10(2), pp. 1-14.
Jahedsaravani, A. Recognition and Tracking of Aerial Targets Using Convolutional Neural Network. Electronic and Cyber Defense, 2023; 10(2): 1-14.

Recognition and Tracking of Aerial Targets Using Convolutional Neural Network

رادار
Article 1, Volume 10, Issue 2 - Serial Number 28, January 2023, Pages 1-14    PDF (1.21 M)
Document Type: Original Article
Author
Ali Jahedsaravani*
Assistant Professor, Khatam Al Anbia Air Defense University, Tehran, Iran
Receive Date: 08 September 2022,  Revise Date: 17 December 2022,  Accept Date: 01 January 2023 
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
Aerial Targets; Target Recognition; Target Tracking; CNN; RCNN
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