Rapid Object Depth Estimation From Position-Referenced EMI Data Using Machine Learning

Šimić, Marko; Ambruš, Davorin; Bilas, Vedran

doi:10.1109/jsen.2023.3234143

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Multiclass and binary 'threat or non-threat' classification of the objects achieved 98% accuracy (with zero false negatives), and was trained on simulations and tested on measurements. A 1D CNN was used to estimate the depth of metallic objects using a pulse induction metal detector [80,81]. In [82], linear (perceptron, multiclass logistic regression) and nonlinear (neural network, 1D CNN, 2D CNN) machine learning algorithms were used to classify metallic objects using EMI data generated by a Heaviside stepoff pulse.…”

Section: Machine Learning Classification Of Metallic Objects Using Em...mentioning

confidence: 99%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawaterirradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated datawere generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the spherein 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeablematerials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receivercoil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms tonoise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression,which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models.Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram dataused for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heavisidestep-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects,including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified usingfinite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion ofelectromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without theuse of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallicspheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paperpresents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.

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Section: Machine Learning Classification Of Metallic Objects Using Em...mentioning

confidence: 99%

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

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Landmine Identification From Pulse Induction Metal Detector Data Using Machine Learning

Šimić,

Ambruš,

Bilas

2023

IEEE Sens. Lett.

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Inversion-Based Magnetic Polarizability Tensor Measurement From Time-Domain EMI Data

Šimić

Ambruš

Bilas

2023

IEEE Trans. Instrum. Meas.

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Rapid Object Depth Estimation From Position-Referenced EMI Data Using Machine Learning

Cited by 3 publications

References 32 publications

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses

Landmine Identification From Pulse Induction Metal Detector Data Using Machine Learning

Inversion-Based Magnetic Polarizability Tensor Measurement From Time-Domain EMI Data

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