Landmine Identification From Pulse Induction Metal Detector Data Using Machine Learning

Šimić, Marko; Ambruš, Davorin; Bilas, Vedran

doi:10.1109/lsens.2023.3307091

Cited by 4 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A 1D CNN was used on EMI data, including metallic spheres, cuboids, and cylinders, to classify time-domain MPT features [79]. 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.…”

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

confidence: 99%

See 1 more Smart Citation

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

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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.

show abstract

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

confidence: 99%

“…Šimić et al [79] used a 1D CNN to infer an object class from time-domain MPT features. A 98% accuracy was achieved for 7 metallic objects in air in a laboratory.…”

Section: Comparison With Other Researchmentioning

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.

View full text Add to dashboard Cite

show abstract

“…Machine learning approaches have been applied to the EMI sensing problem. An EMI system in a laboratory and a 1D CNN was used to classify 7 small hidden metallic objects from time-domain magnetic polarizability tensor features, which included spheres, cuboids, and cylinder shapes [55]. The 1D CNN was trained on simulations and tested on measurements, and achieved 98% accuracy (with zero false negatives) for both multiclass and binary 'threat or non-threat' classification problems.…”

Section: Introductionmentioning

confidence: 99%

Machine learning classification of metallic objects using pulse induction electromagnetic data

Thomas,

Salmon,

Holloway

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

This paper presents the classification of metallic objects using total and scattered pulse induction electromagnetic data, with a classification accuracy greater than 90%. Machine learning classification is applied to raw electromagnetic induction (EMI) data without the use of a physics-based model. The EMI method is applied to 8 metallic objects placed at increasing distances from 10–55 mm to the EMI sensing system. The EMI sensing system consists of two RL circuits placed in close proximity. Metallic objects are classified using linear algorithms including a perceptron and multiclass logistic regression, and nonlinear algorithms including a neural network,a 1D and 2D convolutional neural network (CNN). EMI data was collected using an experiment in an electromagnetically shielded laboratory. Feature maps are presented that explain the salient components of the EMI data used by the 1D and 2D CNN.

show abstract

“…Aside from radar-based systems, there are several other approaches to designing a sensing system for humanitarian demining. This primarily involves the use of magnetometers and metal detectors [11,16], which aim to detect the magnetic field generated by buried metal pieces in the presence of a time-varying electromagnetic field. Although this method is frequently used, it is often difficult to distinguish landmines from other metal objects, and thus the sole use of metal detectors leads to a high false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

A Radar Echo Simulator for the Synthesis of Randomized Training Data Sets in the Context of AI-Based Applications

Schorlemer,

Altholz,

Barowski

et al. 2024

Sensors

View full text Add to dashboard Cite

Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be generated by fast simulators. This procedure is well studied and allows filling possible gaps in the training data, which can further improve the results of a machine learning model. For this reason, this article deals with the development of a two-dimensional electromagnetic field simulator for modeling the response of a radar sensor in an imaging system based on the synthetic aperture radar principle. The creation of completely random scenes is essential to achieve data sets with large variance. Therefore, special emphasis is placed on the development of methods that allow creating random objects, which can then be assembled into an entire scene. In the context of this contribution, we focus on humanitarian demining with regard to improvised explosive devices using a ground-penetrating radar system. This is an area where the use of trained classifiers is of great importance, but in practice, there are little to no labeled datasets for the training process. The simulation results show good agreement with the measurement results obtained in a previous contribution, demonstrating the possibility of enhancing sparse training data sets with synthetic data.

show abstract

Landmine Identification From Pulse Induction Metal Detector Data Using Machine Learning

Cited by 4 publications

References 18 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

Machine learning classification of metallic objects using pulse induction electromagnetic data

A Radar Echo Simulator for the Synthesis of Randomized Training Data Sets in the Context of AI-Based Applications

Contact Info

Product

Resources

About