A deep neural network based method for magnetic anomaly detection
A deep neural network based method for magnetic anomaly detection
复制标题
基于深度神经网络的磁异常检测方法
DOI：
10.1049/smt2.12084
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发表时间：
2021-10
影响因子：
1.4
通讯作者：
Li Qiong
中科院分区：
文献类型：
--
作者：
Wang Yizhen;Han Qi;Zhao Guanyi;Li Minghui;Zhan Dechen;Li Qiong
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.
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影响因子：
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作者：
Dandan Zhao;Zhiyong Guo;Jianfen Du;Liu Zhongxiang;Xu Wei;Gaofei Liu
通讯作者：
Dandan Zhao;Zhiyong Guo;Jianfen Du;Liu Zhongxiang;Xu Wei;Gaofei Liu
影响因子：
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作者：
G. Noriega;Andre Marszalkowski
通讯作者：
G. Noriega;Andre Marszalkowski
DOI：
10.4018/978-1-7998-1192-3.ch008
发表时间：
2020
期刊：
Advances in Systems Analysis, Software Engineering, and High Performance Computing
影响因子：
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作者：
Menaga D.;R. S.
通讯作者：
Menaga D.;R. S.
DOI：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
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作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
DOI：
10.4018/978-1-5225-7862-8.ch007
发表时间：
2019
期刊：
Handbook of Research on Deep Learning Innovations and Trends
影响因子：
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作者：
K. Lakhtaria;Darshankumar Modi
通讯作者：
K. Lakhtaria;Darshankumar Modi