A deep neural network based method for magnetic anomaly detection

Abstract Magnetic anomaly detection (MAD) is a technique to find ferromagnets hiding in strong and complicated magnetic background. In many practical cases, the targets are very far from the detection sensor, which leads to low signal‐to‐noise ratio (SNR) and high detection difficulty. Most of the c...

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Autores principales:	Yizhen Wang, Qi Han, Guanyi Zhao, Minghui Li, Dechen Zhan, Qiong Li
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2022
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/7246da67cecb4c0ca705c2e666166f08
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spelling	oai:doaj.org-article:7246da67cecb4c0ca705c2e666166f082021-12-01T10:55:36ZA deep neural network based method for magnetic anomaly detection1751-88301751-882210.1049/smt2.12084https://doaj.org/article/7246da67cecb4c0ca705c2e666166f082022-01-01T00:00:00Zhttps://doi.org/10.1049/smt2.12084https://doaj.org/toc/1751-8822https://doaj.org/toc/1751-8830Abstract Magnetic anomaly detection (MAD) is a technique to find ferromagnets hiding in strong and complicated magnetic background. In many practical cases, the targets are very far from the detection sensor, which leads to low signal‐to‐noise ratio (SNR) and high detection difficulty. Most of the current methods determine the existence of target by some approaches based on signal analysis, such as the orthogonal basis function (OBF) and the minimum entropy (ME). However, although these methods consume low resources, the detection performances are not satisfactory enough. In recent years, due to the increase of computer capability, complex methods become applicable in MAD. In this study, a deep neural network (DNN) is adopted to detect the magnetic anomalies. The DNN has shown its better ability to represent natural data in many applications. A feature automatically learned by a DNN from data in the raw form is more effective for detecting target signals and suppressing irrelevant variations. Herein, a convolutional network with residual structure to implement the feature extraction is designed and an MAD method based on it is proposed. Through the semi‐real tests, the proposed method exhibits a strong capability to extract features and shows excellent performances on detection.Yizhen WangQi HanGuanyi ZhaoMinghui LiDechen ZhanQiong LiWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIET Science, Measurement & Technology, Vol 16, Iss 1, Pp 50-58 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yizhen Wang Qi Han Guanyi Zhao Minghui Li Dechen Zhan Qiong Li A deep neural network based method for magnetic anomaly detection
description	Abstract Magnetic anomaly detection (MAD) is a technique to find ferromagnets hiding in strong and complicated magnetic background. In many practical cases, the targets are very far from the detection sensor, which leads to low signal‐to‐noise ratio (SNR) and high detection difficulty. Most of the current methods determine the existence of target by some approaches based on signal analysis, such as the orthogonal basis function (OBF) and the minimum entropy (ME). However, although these methods consume low resources, the detection performances are not satisfactory enough. In recent years, due to the increase of computer capability, complex methods become applicable in MAD. In this study, a deep neural network (DNN) is adopted to detect the magnetic anomalies. The DNN has shown its better ability to represent natural data in many applications. A feature automatically learned by a DNN from data in the raw form is more effective for detecting target signals and suppressing irrelevant variations. Herein, a convolutional network with residual structure to implement the feature extraction is designed and an MAD method based on it is proposed. Through the semi‐real tests, the proposed method exhibits a strong capability to extract features and shows excellent performances on detection.
format	article
author	Yizhen Wang Qi Han Guanyi Zhao Minghui Li Dechen Zhan Qiong Li
author_facet	Yizhen Wang Qi Han Guanyi Zhao Minghui Li Dechen Zhan Qiong Li
author_sort	Yizhen Wang
title	A deep neural network based method for magnetic anomaly detection
title_short	A deep neural network based method for magnetic anomaly detection
title_full	A deep neural network based method for magnetic anomaly detection
title_fullStr	A deep neural network based method for magnetic anomaly detection
title_full_unstemmed	A deep neural network based method for magnetic anomaly detection
title_sort	deep neural network based method for magnetic anomaly detection
publisher	Wiley
publishDate	2022
url	https://doaj.org/article/7246da67cecb4c0ca705c2e666166f08
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_version_	1718405239567024128

A deep neural network based method for magnetic anomaly detection

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