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\begin{document}

\title{Artificial intelligence to detect buried objects}



\author[1]{Ali Gharamohammadi }%
\author[2]{George Shaker }%
\author[2]{Mark Crowley }%
\affil[1]{Unversity of Waterloo}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\begin{abstract}
The detection of targets under the ground is an important procedure that
is typically performed by a human non-automatically. Recent studies have
automated this process using artificial intelligence (AI) based on radar
images. There are three main steps before feeding reconstructed radar
images to a neural network. The first step is segmentation which can
make the detection task straightforward. We have proposed an Otsu-based
segmentation algorithm in this paper. The proposed segmentation
algorithm is effectively able to distinguish between all the targets. In
the second step before employing AI to detect targets, a local sliding
window has been taken into consideration to improve the results. The
image is divided into smaller parts by this sliding window after it has
been reconstructed. In the third step, two different methods have been
considered for data augmentation. The first method is a novel approach
for generating synthetic radar data. It is applied before radar image
reconstruction based on the summation of two receivers' signals with
different coefficients. In the second augmentation method, some
conventional data augmentation methods like flip and rotating are
applied to complete this task. To discriminate targets from background,
it is necessary to classify input images to AI-based aproaches. This
task can be accomplished by classical machine learning approaches like
the scalar vector machine (SVM). Gabor filters have been utilized in
this paper to extract the features. There also exist two classification
approaches using convolutional neural networks (CNN) to automatically
detect targets after image reconstruction. Two different CNN have been
implemented. Without data augmentation, the SVM-based approach works
better than CNN, and its accuracy is 86.9\%. Overall, the second CNN
algorithm outperformed SVM after the data augmentation by reaching 96\%
accuracy.%
\end{abstract}%



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\textbf{Hosted file}

\verb`AI GPR_03_04_2023 (2).docx`  available at \url{https://authorea.com/users/686924/articles/681485-artificial-intelligence-to-detect-buried-objects}

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