A Study on the Performance Comparison of Neural Network for Metal Detection Based on MI Sensor

Abstract

This paper is a study on the efficiency of the filtering method of signal processing and the metal detection method using deep learning for data obtained from multiple MI sensors. The MI sensor is a principle that detects changes in magnetic field and is a passive sensor that detects metal objects. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small, so there is a limit to the detectable distance. In order to effectively detect and analyze this, a method using deep learning was applied. In addition, the performance of the deep learning model was compared and analyzed using the filtering method of signal processing. In this paper, the detection performance of CNN and RNN networks was compared and analyzed from the data extracted from the self-impedance sensor. The RNN model showed higher performance than the CNN model. However, in the shallow stage, the CNN model showed higher performance than the RNN model.