{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T14:03:06Z","timestamp":1775570586281,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801511"],"award-info":[{"award-number":["61801511"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BK20180580"],"award-info":[{"award-number":["BK20180580"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KYCX20_0311"],"award-info":[{"award-number":["KYCX20_0311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["61801511"],"award-info":[{"award-number":["61801511"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20180580"],"award-info":[{"award-number":["BK20180580"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["KYCX20_0311"],"award-info":[{"award-number":["KYCX20_0311"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["61801511"],"award-info":[{"award-number":["61801511"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["BK20180580"],"award-info":[{"award-number":["BK20180580"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYCX20_0311"],"award-info":[{"award-number":["KYCX20_0311"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east\u2013west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method\u2019s recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification.<\/jats:p>","DOI":"10.3390\/s22197653","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"7653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure"],"prefix":"10.3390","volume":"22","author":[{"given":"Songtong","family":"Han","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7720-9801","authenticated-orcid":false,"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Disaster Prevention and Mitigation of Explosion and Impact, The Army Engineering University of PLA, Nanjing 210094, China"}]},{"given":"Zhu","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Public Security, Sichuan Police College, Luzhou 646000, China"}]},{"given":"Chunwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1007\/s10712-020-09598-1","article-title":"Geomagnetic field processes and their implications for space weather","volume":"41","author":"Mandea","year":"2020","journal-title":"Surv. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e202000212","DOI":"10.1002\/zamm.202000212","article-title":"Mechanical aspects of Maxwell nanofluid in dynamic system with irreversible analysis","volume":"101","author":"Khan","year":"2021","journal-title":"ZAMM-J. Appl. Math. Mech.\/Zeitschrift f\u00fcr Angewandte Mathematik und Mechanik"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Buschow, K.H.J., and Boer, F.R. (2003). Physics of Magnetism and Magnetic Materials, Kluwer Academic\/Plenum Publishers.","DOI":"10.1007\/b100503"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"075102","DOI":"10.1088\/0957-0233\/24\/7\/075102","article-title":"Calibration of a fluxgate magnetometer array and its application in magnetic object localization","volume":"24","author":"Pang","year":"2013","journal-title":"Meas. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"01045","DOI":"10.1051\/e3sconf\/201913101045","article-title":"The application of magnetometers and electromagnetic induction sensors in UXO detection","volume":"131","author":"Han","year":"2019","journal-title":"E3S Web Conf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shan, S., Zhang, H., Zhou, S., and Dai, Z. (2020, January 25\u201327). On Real-time Tracking for Underwater Magnetic Target with Extension Considerations. Proceedings of the2020 7th International Forum on Electrical Engineering and Automation, Hefei, China.","DOI":"10.1109\/IFEEA51475.2020.00164"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1007\/s40808-021-01155-y","article-title":"Comparative depth estimates and modeling of magnetic anomalies over the Nkalagu area, Southeastern Nigeria","volume":"8","author":"Obiora","year":"2022","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"035113","DOI":"10.1063\/5.0039894","article-title":"A multi-magneto-inductive sensor array system for real-time magnetic field imaging of ferromagnetic targets","volume":"92","author":"Liu","year":"2021","journal-title":"Rev. Sci. Instrum."},{"key":"ref_9","first-page":"1","article-title":"Integrated compensation and rotation alignment for three-axis magnetic sensors array","volume":"54","author":"Li","year":"2018","journal-title":"IEEE Trans. Magn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"166450","DOI":"10.1016\/j.jmmm.2020.166450","article-title":"Magnetic dipole localization and magnetic moment estimation method based on normalized source strength","volume":"502","author":"Yin","year":"2020","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2154031","DOI":"10.1142\/S0218001421540318","article-title":"A Calculation Method of Magnetic Anomaly Field Distribution of Ellipsoid with Arbitrary Posture","volume":"35","author":"Han","year":"2021","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_12","first-page":"2066","article-title":"Modeling method using simplest multiple magnetic dipoles equivalence","volume":"43","author":"Jin","year":"2021","journal-title":"Syst. Eng. Electron. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1109\/TMAG.2006.879151","article-title":"A closed-form formula for magnetic dipole localization by measurement of its magnetic field and spatial gradients","volume":"42","author":"Nara","year":"2006","journal-title":"IEEE Trans. Magn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.jmmm.2006.01.027","article-title":"On uniqueness of solution of a reverse problem of magnetic location","volume":"305","author":"Kasatkin","year":"2006","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1109\/TMAG.2010.2091964","article-title":"Recursive Bayesian method for magnetic dipole tracking with a tensor gradiometer","volume":"47","author":"Birsan","year":"2010","journal-title":"IEEE Trans. Magn."},{"key":"ref_16","first-page":"1032","article-title":"A fast linear algorithm for magnetic dipole localization using total magnetic field gradient","volume":"18","author":"Fan","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_17","first-page":"1","article-title":"A real-time magnetic dipole localization method based on cube magnetometer array","volume":"55","author":"Liu","year":"2019","journal-title":"IEEE Trans. Magn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"166274","DOI":"10.1016\/j.jmmm.2019.166274","article-title":"A closed-form formula for magnetic dipole localization by measurement of its magnetic field vector and magnetic gradient tensor","volume":"499","author":"Yin","year":"2020","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/TGRS.2006.872905","article-title":"Magnetic models of unexploded ordnance","volume":"44","author":"Billings","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jappgeo.2009.10.002","article-title":"Location and depth estimation of point-dipole and line of dipoles using analytic signals of the magnetic gradient tensor and magnitude of vector components","volume":"70","year":"2010","journal-title":"J. Appl. Geophys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/36.917899","article-title":"Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys","volume":"39","author":"Hart","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nelson, H.H., Steinhurst, D.A., Barrow, B., Bell, T., Khadar, N., SanFilipo, B., and Won, I.J. (2007). Enhanced UXO Discrimination Using Frequency-Domain Electromagnetic Induction, Environmental Security Technology Certification Program Office (DOD).","DOI":"10.21236\/ADA469893"},{"key":"ref_23","first-page":"92","article-title":"Live-site UXO classification studies using advanced EMI and statistical models","volume":"8017","author":"Shamatava","year":"2011","journal-title":"Detect. Sens. Mines Explos. Objects Obs. Targets XVI SPIE"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.1109\/TGRS.2013.2287510","article-title":"Camp Butner live-site UXO classification using hierarchical clustering and Gaussian mixture modeling","volume":"52","author":"Bijamov","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","unstructured":"Billings, S.D., Pasion, L.R., and Oldenburg, D.W. (2002, January 3\u20136). Inversion of magnetics for UXO discrimination and identification. Proceedings of the 2002 UXO Forum, Orlando, FL, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TGRS.2011.2112772","article-title":"Incorporating uncertainty in unexploded ordnance discrimination","volume":"49","author":"Beran","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1093\/gji\/ggz421","article-title":"Inference of unexploded ordnance (UXO) by probabilistic inversion of magnetic data","volume":"220","author":"Wigh","year":"2020","journal-title":"Geophys. J. Int."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"187202","DOI":"10.1109\/ACCESS.2020.3030676","article-title":"Detection and classification of multi-magnetic targets using mask-RCNN","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","unstructured":"Walter, T. (2012). Introduction to Electrodynamics and Radiation, Elsevier."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wiegert, R., Lee, K., and Oeschger, J. (2008, January 15\u201318). Improved magnetic STAR methods for real-time, point-by-point localization of unexploded ordnance and buried mines. Proceedings of the OCEANS 2008, Quebec City, QC, Canada.","DOI":"10.1109\/OCEANS.2008.5152073"},{"key":"ref_31","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Sardinia, Italy."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_33","unstructured":"Saxe, A.M., McClelland, J.L., and Ganguli, S. (2013). Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv."},{"key":"ref_34","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","unstructured":"Schubert, G. (2015). The present and future geomagnetic field. Treatise on Geophysics, Elsevier. [2nd ed.]."},{"key":"ref_38","unstructured":"Schubert, G. (2015). Treatise on Geophysics, Elsevier."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"938","DOI":"10.3390\/ijerph7030938","article-title":"Mechanisms of geomagnetic field influence on gene expression using influenza as a model system: Basics of physical epidemiology","volume":"7","author":"Zaporozhan","year":"2010","journal-title":"Int. J. Environ. Res. Public Health"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7653\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:48:38Z","timestamp":1760143718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7653"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,9]]},"references-count":39,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197653"],"URL":"https:\/\/doi.org\/10.3390\/s22197653","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,9]]}}}