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Classifying Buried Objects by Combining CNN with Fourier Transform
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 34 |
| Year of Publication: 2023 |
| Authors: Ahmed M.D.E. Hassanein, Mostafa M.M. Aboalamayem |
10.5120/ijca2023923118
|
Ahmed M.D.E. Hassanein, Mostafa M.M. Aboalamayem . Classifying Buried Objects by Combining CNN with Fourier Transform. International Journal of Computer Applications. 185, 34 ( Sep 2023), 17-22. DOI=10.5120/ijca2023923118
Abstract
Identifying buried objects needs signal processing which requires high computational power. In this paper, the frequency domain and time domain signals are used in combination with Convolutional Neural Network to classify the buried objects represented by the signals. The Fourier transform is used to change from the frequency domain to the time domain as it gives a holistic view of the environment around the antennas. The proposed method doesn’t require to go into any complex signal processing steps which can save a lot of effort. A Bow Tie antenna is used to take measurements of five different setups. One setup is when the environment between the two antennas is filled with air, another is when it is filled with sand and another is when the sand contains buried rod at depth 3, 5 and 10cm. Each setup represents a class. The resulting measurements are in the frequency domain. Fourier transform is used to obtain those measurements in the time domain. The aim is to use a proposed CNN network to classify the frequency domain measurements to their corresponding classes. Another aim is to see the effect of using the frequency domain signals along with the time domain signals to classify them. The frequency domain achieved 87% accuracy in classifying the signals. While, the frequency domain and time domain signals achieved 93% accuracy.
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