Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

Abstract

Artificial Neural Network (ANN) approaches are applied to detect and determine the object class using a special set of the UltraWideBand (UWB) pulse Ground Penetrating Radar (GPR) sounding results. It used the results of GPR sounding with the antenna system, consisting of one radiator and four receiving antennas located around the transmitting antenna. The presence of four receiving antennas and, accordingly, the signals received from four spatially separated positions of the antennas provide a collection of signals received after reflection from an object at different angles and, due to this, to determine the location of the object in a coordinate system, connected to the antenna. We considered the sums and differences of signals received by two of the four antennas in six possible combinations: (1 and 2, 1 and 3, 2 and 3, 1 and 4, etc.). These combinations were then stacked sequentially one by one into one long signal. Synthetic signals constructed in such a way contain many more notable differences and specific information about the class to which the object belongs as well as the location of the searched object compared to the signals obtained by an antenna system with just one radiating and one receiving antenna. It therefore increases the accuracy in determining the object’s coordinates and its classification. The pulse radiation, propagation, and scattering are numerically simulated by the finite difference time domain (FDTD) method. Results from the experiment on mine detection are used to examine ANN too. The set of signals from different objects having different distances from the GPR was used as a training and testing dataset for ANN. The training aims to recognize and classify the detected object as a landmine or other object and to determine its location. The influence of Gaussian noise added to the signals on noise immunity of ANN was investigated. The recognition results obtained by using an ANN ensemble are presented. The ensemble consists of fully connected and recurrent neural networks, gated recurrent units, and a long-short term memory network. The results of the recognition by all ANNs are processed by a meta network to provide a better quality of underground object classification.

Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging

“…Several studies have utilized deep learning architectures to enhance the accuracy and efficiency of landmine detection. Following the footsteps of Silva et al [45], Pryshchenko et al [46] adopted a fusion approach, analyzing signals with fully connected neural networks (FCNN), recurrent neural networks (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) models for ultrawideband GPR antennas. The results of these models were fused for improved performance.…”

“…In the context of landmine detection, real-time capabilities are paramount for practical deployment, enabling timely responses to potential threats. While there exist several noteworthy works in the field of landmine detection, most of them are in a laboratory condition [47], are not tested on out of distribution (OOD) data [13,47], only considered navigation problems [17], and are, in general, not optimized for real-time applications [29,[45][46][47]. Our research is primarily motivated by the need for real-time landmine detection in diverse scenarios.…”

Dielectric cylinder discrimination with ANN using phase information of synthesized time-domain response

“…A key problem is discrimination with size estimation. Neural networks are a powerful tool for object recognition [3][4][5]. The problem is to obtain a reflected signal for a set of objects with various diameters with small step in a comparatively simple way for network training.…”

Integrating Humanitarian Demining with Remote Control Tech in Educational Process

“…Therefore, the issue of finding a safe, effective, fast and inexpensive way of demining is relevant (Looney, 2021). Remote demining using robots and UAVs is one of them (Pryshchenko, 2022). It was proved (Tuohy, 2023) that the earlier the attention of students is drawn to the technical and social aspects of humanitarian mine action, the more effective it will be.…”