Abstract
The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.
Highlights
Deep learning [1,2] has been proven effective and successful in many fields in science and engineering such as medical diagnoses, image [3,4,5,6], signal [7,8] and speech recognition, financial services, autopilot in automotive scenarios, and many other engineering and medical applications
In the case of periodic signals, deep learning determines the correlation between the previous data and existing data, or it learns the continuity of data in time-series data, thereby providing high detection and prediction rates
The magnetic impedance (MI) effect [9] is a phenomenon in which the impedance of a magnetic object changes according to the strength of an external magnetic field
Summary
Deep learning [1,2] has been proven effective and successful in many fields in science and engineering such as medical diagnoses, image [3,4,5,6], signal [7,8] and speech recognition, financial services, autopilot in automotive scenarios, and many other engineering and medical applications. The magnetic impedance (MI) effect [9] is a phenomenon in which the impedance of a magnetic object changes according to the strength of an external magnetic field. The pulsed magnetic field can be employed in various applications that use sensors, such as geomagnetic measurement, drone control, electronic mapping, foreign object detection, and autonomous driving. It can be applied as a metal detector in foreign matter detection. It is used in mine detection, metal separation, and security checkpoints at airports
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