Classification of items in a walk-through metal detector using time series of eigenvalues of the polarizability tensor

Kauppila, Jarmo; Ala-Kleemola, T.; Vihonen, Juho; Jylhä, Juha; Ruotsalainen, Marja; Järvi, Ari; Visa, Ari

doi:10.1117/12.818424

Cited by 5 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, if one was to put a knife in a metallic box, the knife may not be seen by a scanner. It should be noted that this threat definition differs from earlier studies [3], and therefore, comparing the results Table 3. Results for TC1 and TC2.…”

Section: Test Case 1 (Tc1): Threats Vs Innocuous Objectsmentioning

confidence: 91%

“…This paper describes a K-nearest neighbour (KNN) based classifier algorithm, first proposed by [3], and studies the extent to which it can distinguish between different objects that may typically be encountered with a WTMD. This is demonstrated with several test cases.…”

Section: Introductionmentioning

confidence: 99%

“…However, there have been few attempts to add intelligence to them. Steps to this direction have been taken by Kauppila et al [3], and Marsh et al [4] that have made it possible to determine the polarisability tensor and the path of a metallic object passing through a real working WTMD. This is based on an inversion algorithm that is covered in section 3.2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

KNN classification of metallic targets using the magnetic polarizability tensor

Makkonen¹,

Marsh²,

Vihonen³

et al. 2014

Meas. Sci. Technol.

View full text Add to dashboard Cite

Walk-through metal detectors are used at check points for preventing personnel and passengers from carrying threatening metallic objects, such as knives and guns into a secure area. These systems are capable of detecting small metallic items, such as handcuff keys and blades, but are unable to distinguish accurately between threatening objects and innocuous items. This paper studies the extent to which a K-Nearest Neighbour-classifier can distinguish various kinds of metallic objects, such as knives, shoe shanks, belts and containers. The classifier uses features extracted from the magnetic polarisability tensor, which represents the electromagnetic properties of the object. The tests include distinguishing threatening objects from innocuous ones, classifying a set of objects into 13 classes, and distinguishing between several similar objects within an object class. A walk-through metal detection system is used as source for the test data, which consists of 835 scans and 67 objects. The results presented show a typical success rate of over 95% for recognizing threats, and over 85% for correct classification. In addition, we have shown that the system is capable of distinguishing between similar objects reliably. Overall, the method shows promise for the field of security screening and suggests the need for further research.

show abstract

Section: Test Case 1 (Tc1): Threats Vs Innocuous Objectsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

KNN classification of metallic targets using the magnetic polarizability tensor

Makkonen¹,

Marsh²,

Vihonen³

et al. 2014

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Neural networks were used to discriminate the weapon type and identify other, daily items. Kauppila et al [6] generates mutually orthogonal homogeneous magnetic fields so that the measured dipole moments allow the classification of even the smallest of the items with a high degree of classification rate (near 90%). Elgwel et al [7] classified alarm-inducing objects based on the decay rate of the induced currents in the object.…”

Section: Research On Wtmd and Bs Devicesmentioning

confidence: 99%

Feasibility of Reducing Operator-to-Passenger Contact for Passenger Screening at the Airport with Respect to the Power Consumption of the System

Kierzkowski

Kisiel

2021

Energies

View full text Add to dashboard Cite

So far, airport security screening has only been analysed in terms of efficiency, level of service, and protection against any acts of unlawful interference. Screening procedures have not yet addressed the need to limit operator-to-passenger contact. However, the pandemic situation (COVID-19) has shown that it is a factor that can be a key protection for the health of passengers and operators. The purpose of this paper was to analyse the feasibility of reducing contact between operators and passengers in the airport security screening system by process management with respect to the power consumption of the system. Experimental research was conducted on a real system. A computer simulation was applied to estimate system performance and power consumption. The paper identifies the important findings that expand upon previous knowledge. The results showed that there are two key factors: the experience of operators and proper system structure. These factors can significantly reduce the number of operator-to-passenger contacts and, in parallel, provide lower energy consumption of the system. The results obtained in this article showed that proper management improves the process by up to 37%. This approach expands the World Health Organization’s policy of prevention against COVID-19 and helps to ensure sustainable process management.

show abstract

“…Metallic objects can be efficiently described using a magnetic polarisability tensor model [1,2]. It has been shown that an inversion algorithm can be used for estimating the tensor model parameters and the trajectory of the object by using measured data from a purpose-built WTMD portal [3].…”

Section: Introductionmentioning

confidence: 99%

Improving reliability for classification of metallic objects using a WTMD portal

Makkonen

Marsh

Vihonen

et al. 2015

Meas. Sci. Technol.

View full text Add to dashboard Cite

In this paper, a walk-through metal detection (WTMD) portal is used for classification of metallic objects. The classification is based on the inversion of the magnetic polarisability tensor (tensor) of the object. The nature of bias and noise components in the tensor are examined by using real walk-through data, and consequently, a novel classifier is introduced. Furthermore, a novel method for detecting poorly inverted tensors is presented, enabling self-diagnostics for the WTMD portal. Based on the results, the novel methods increase the accuracy of metal object classification and have the potential to improve the reliability of a WTMD system.

show abstract

Classification of items in a walk-through metal detector using time series of eigenvalues of the polarizability tensor

Cited by 5 publications

References 0 publications

KNN classification of metallic targets using the magnetic polarizability tensor

KNN classification of metallic targets using the magnetic polarizability tensor

Feasibility of Reducing Operator-to-Passenger Contact for Passenger Screening at the Airport with Respect to the Power Consumption of the System

Improving reliability for classification of metallic objects using a WTMD portal

Contact Info

Product

Resources

About