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Neural Network for Metal Detection Based on Magnetic Impedance Sensor
| Content Provider | MDPI |
|---|---|
| Author | Ha, Sung Jae Lee, Dongwoo Kim, Hoijun Kwon, Soonchul Kim, Eung Jo Yang, Junho Lee, Seunghyun |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 4456 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21134456 |
| Journal | Sensors |
| Issue Number | 13 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-06-29 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Convolutional Neural Network Deep Learning Magnetic Impedance Metal Detection Recurrent Neural Network Sensor Signal Processing |
| Content Type | Text |
| Resource Type | Article |
