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.