Performances of C-BORD’s Tagged Neutron Inspection System for Explosives and Illicit Drugs Detection in Cargo Containers

Sardet, Alix; Pérot, B.; Carasco, C.; Sannié, Guillaume; Moretto, S.; Nebbia, G.; Fontana, Cristiano Lino; Pino, F.

doi:10.1109/tns.2021.3050002

Cited by 10 publications

(11 citation statements)

References 19 publications

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“…Due to the high intensity and long time of manual work, it is easy to cause visual fatigue and affect the final detection accuracy. Research on the digital transformation of traditional wood surface defect detection and quality grading based on artificial intelligence will greatly liberate manual labor, guide the conversion of original manual labor to mental labor, change the production mode of traditional wood screening, and improve industrial efficiency and automation level [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

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In order to solve the problems of image perception and quality decision-making of wood defects with typical bionic intelligent algorithms, the existence of multidimensional degradation factors causes serious real problems of image distortion; the author proposes a wood defect image reconstruction and quality evaluation model based on deep reinforcement learning. The model introduced the deep learning mechanism and realized real-time and efficient reconstruction of multidimensional defect images of different wood by using the deep residual network for iterative training. In this model, a panoramic autonomous perception model was constructed for the fine segmentation and feature extraction of multidimensional defects of different wood and a shared resource pool of wood defect features of the magnitude of big data was constructed. Introduce the reinforcement learning mechanism, use the deep deterministic policy gradient algorithm, and establish a high-dimensional decision mapping between the iterative update of defect features, autonomous decision-making, panoramic visualization, depth prediction, and wood quality evaluation; it realizes the horizontal sharing integration of multidimensional difference wood defect image reconstruction and quality evaluation. The results obtained are as follows: in a typical environment, systematic wood quality evaluation, and autonomous intelligent decision-making performance, the coincidence rate with artificial defect recognition and evaluation efficiency can reach 90% and the loss of the training set can be controlled below 0.2%. Compared with the traditional wood quality grading system, the wood defect image reconstruction, and quality evaluation model system designed by the author, the quality evaluation decision-making efficiency rate was 90.19%, an increase of about 20%, and the system decision-making operation and maintenance loss was 2.23%, a decrease of about 10%. It is proved that the system designed by the author can realize the timely detection of wood quality defects very effectively and save a lot of manpower and material resources.

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Section: Introductionmentioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

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“…New data processing and visualisation approaches have also been developed [65]. To check consistency between measurements and the manifest of the cargo container (declaration of transported goods) and identify materials in a user-friendly way, the three spider charts shown in Figure 30 allow grasping easily their elemental composition and provides a rough classification.…”

Section: C-bord Rapidly Relocatable Tagged Neutron Inspection Systemmentioning

confidence: 99%

“…Further investigations are needed to improve the rejection and detectability of elements with small contributions in the unfolding. Figure 32 shows an example of 10 min measurements with a neutron flux of 5.10 7 n/s [65]. The container is loaded with wood and a 10 kg RDX explosive simulant is hidden at a depth of 30 cm inside the wood matrix.…”

Section: C-bord Rapidly Relocatable Tagged Neutron Inspection Systemmentioning

confidence: 99%

“…Experimental laboratory and field tests of C-BORD RRTNIS [65,66] have demonstrated its capability to detect less than 20 kg of explosives or narcotics in iron or wood matrices having a density of 0.2 g.cm À3 . These performances are a real step forward compared to EURITRACK system designed to detect 100 kg of TNT in the middle of a 0.2 g.cm À3 iron cargo.…”

Section: C-bord Rapidly Relocatable Tagged Neutron Inspection Systemmentioning

confidence: 99%

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Sea container inspection with tagged neutrons

Pérot

Carasco

Eléon

et al. 2021

EPJ Nuclear Sci. Technol.

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Neutron inspection of sea-going cargo containers has been widely studied in the past 20 yr to non-intrusively detect terrorist threats, like explosives or Special Nuclear Materials (SNM), and illicit goods, like narcotics or smuggling materials. Fast 14 MeV neutrons are produced by a portable generator with the t(d, n)α fusion reaction, and tagged in both direction and time thanks to the alpha particle detection. This Associated Particle Technique (APT) allows focusing inspection on specific areas of interest in the containers, previously identified as containing suspicious items with X-ray radiographic scanners or radiation portal monitors. We describe the principle of APT for non-nuclear material identification, and for nuclear material detection, then we provide illustrations of the performances for 10 min inspections with significant quantities (kilograms) of explosives, illicit drugs, or SNM, in different cargo cover loads (e.g. metallic, organic, or ceramic matrices).

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“…The European project C-BORD (effective Container inspection at BORDer control points) was aimed at developing and testing a comprehensive cost-effective solution for the generalized inspection of large-volume freight containers [1][2][3][4][5][6][7][8][9][10]. In order to contribute to the protection of EU borders, the C-BORD collaboration developed a framework of Non-Intrusive Inspection (NII) technologies.…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive inspection of cargo containers using the C-BORD Rapidly Relocatable Tagged Neutron Inspection System

et al. 2022

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The European H2020 project, entitled “effective Container inspection at BORDer control points” (C-BORD), aims to develop a framework of Non-Intrusive Inspection (NII) technologies, for containers and large-volume freight at the EU borders. In this article the first results of the field trials of the Rapidly Relocatable Tagged Neutron Inspection System (RRTNIS) are reported. The tests were carried out at the customs administration of the Netherlands' (DCA) facility in the seaport of Rotterdam, the Netherlands. For the tests mock-up cargo containers were prepared. The containers were analyzed using the whole set of NII subsystems of the C-BORD framework. Each container underwent a session of subsequent scans with all the subsystems, in order to realistically reproduce the normal flow of inspections at the customs facility. The RRTNIS was tested under these realistic conditions, as well. The system was able to separate and identify the target materials, in mock-up containers, and improve the state-of-the-art in terms of the detected quantities.

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Performances of C-BORD’s Tagged Neutron Inspection System for Explosives and Illicit Drugs Detection in Cargo Containers

Cited by 10 publications

References 19 publications

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sea container inspection with tagged neutrons

Non-intrusive inspection of cargo containers using the C-BORD Rapidly Relocatable Tagged Neutron Inspection System

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