Recognition of Explosive Devices Based on the Detectors Signal Using Machine Learning Methods

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Abstract
Due to the full-scale invasion of Ukraine, the demining and methods of automating this process are much more relevant nowadays. Robots and algorithms can help solve this problem. In this study, the methods of automated detection of explosive devices were investigated. We reviewed various modern methods of demining the territory. We analyzed the principle of operation of metal detectors and GPR. After that, we found out the advantages and disadvantages of each of these devices. We investigated which of these devices gives more accurate data about the object detected on the ground and which data is better for training the model. As a result, an information system was developed based on a convolutional neural network and an autoencoder for the automated classification of explosive devices. We used a set of scanned ground images obtained from ground- penetrating radar (GPR) for the autoencoder input data. We received an accuracy of 97.83%. The algorithm described in this study can help in demining the territories of Ukraine. Keywords 1 Metal detector, landmines, GPR, machine learning, classification.

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