Research on magnetic detection target recognition method based on residual network combined with magnetic moment estimation

被引:0
|
作者
Wen, Zhu [1 ]
Han, Songtong [2 ]
Gao, Chengwei [2 ]
Xu, Lumei [2 ]
Fang, Ying [1 ]
Ding, Luyong [1 ]
机构
[1] Yibin Vocat & Tech Coll, 300 Yuhua Rd, Yibin 644003, Peoples R China
[2] Heilongjiang North Tools Co Ltd, 56 Xingye Rd, Mudanjiang 157011, Peoples R China
关键词
Unexploded ordnance; Magnetic detection; Magnetic moment estimation; Dipole target; Residual network; CLASSIFICATION; UXO; DISCRIMINATION; LOCALIZATION; INVERSION;
D O I
10.1016/j.measurement.2024.114550
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The unexploded ordnance left over from war conflicts and live -fire exercises has a strong invisibility, posing a serious threat to the safety of local residents ' lives and property. Based on the idea of removing useless parameters and regressing the main parameters, we decompose the magnetization field of the dipole target into the product of the basis function and the simplified function, and invert the magnetic moment of the dipole. A method that can quickly estimate the magnetic moment of the detection target, and a detection target recognition method using residual network combined with fast magnetic moment estimation is proposed. Through field experiments for comparison and analysis, the Accuracy of this method for detecting three types of targets can reach 89.7%, Recall can reach 80%, and Precision can reach 90.3%, which provides a technical reference for target type recognition of unexploded ordnance based on magnetic detection.
引用
收藏
页数:12
相关论文
共 50 条
  • [31] An Intelligent Denoising Method for Nuclear Magnetic Resonance Logging Measurement Based on Residual Network
    Gao, Yang
    Wei, Meng
    Zhu, Jinbao
    Wang, Yida
    Zhang, Yang
    Lin, Tingting
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
  • [32] Research on Quantification Method of Ellipsoidal Defects Based on Leakage Magnetic Detection
    Gao, Pengfei
    Geng, Hao
    Yang, Lijian
    Zheng, Fuyin
    Su, Yuming
    IEEE SENSORS JOURNAL, 2024, 24 (09) : 14503 - 14518
  • [33] Radar air target recognition based on deep residual shrinkage network
    Yin, Jianguo
    Sheng, Wen
    Jiang, Wei
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2024, 46 (09): : 3012 - 3018
  • [34] A neural network based estimation method for magnetic shielding at extremely low frequencies
    Umurkan, Nurettin
    Koroglu, Selim
    Kilic, Osman
    Adam, Ali A.
    EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (04) : 3195 - 3201
  • [35] SAR Image Target Recognition Based on Improved Residual Attention Network
    Shi Baodai
    Zhang Qin
    Li Yao
    Li Yuhuan
    LASER & OPTOELECTRONICS PROGRESS, 2021, 58 (08)
  • [36] Research on Target Detection and Recognition Based on Micro Doppler
    Zheng, Yu
    AGRO FOOD INDUSTRY HI-TECH, 2017, 28 (01): : 768 - 772
  • [37] Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method
    Hong, Feng
    Liu, Chengwei
    Guo, Lijuan
    Chen, Feng
    Feng, Haihong
    APPLIED SCIENCES-BASEL, 2021, 11 (04): : 1 - 12
  • [38] Novel magnetic method for the detection of residual curvature in electrical steel
    Hall, JP
    Moses, AJ
    Irons, T
    Snell, D
    JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, 2003, 254 : 64 - 66
  • [39] Direct moment estimation of intensity distribution of magnetic fields with quantum sensing network
    Kasai, Hiroto
    Takeuchi, Yuki
    Matsuzaki, Yuichiro
    Tokura, Yasuhiro
    New Journal of Physics, 2024, 26 (12)
  • [40] Defect Recognition Method with Low False Negative Rate Based on Combined Target Detection Framework
    Luo P.
    Wang B.
    Ma H.
    Ma F.
    Wang H.
    Zhu D.
    Gaodianya Jishu/High Voltage Engineering, 2021, 47 (02): : 454 - 462