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Using cosmic rays for better, more portable and efficient analysis and detection for customs

Project description

Mobile solution with high detection capabilities for shipments

The increasing volume of shipped goods is leading to a shift from centralised to smaller logistics companies. However, these smaller locations often have limited detection capabilities, making them vulnerable to the transport of illicit goods. Current technologies are ineffective. X-ray technologies, for example, cannot precisely analyse the material content of small shipments. The EU-funded CosmoPort project aims to develop an innovative and highly effective solution for the next generation of scanner systems. It will use Atmospheric Ray Tomography (ART) combined with advanced Machine Learning-based risk assessment tools. While muon tomography is already in use, this project will introduce the first mobile solution that integrates AI/ML tools to enhance material and object classification.

Objective

The number of goods and parcels shipped is growing with unstoppable speed. With more individual shipments the means of logistics have changed from central locations to small logistics companies, postal agencies, airports, ports, private vehicles etc. However, the analysis and detection capabilities of such locations are very low, making it an easy target for transporting illicit goods.
Currently, there are no effective technologies available that would be able to cope with this growing problem. Despite the widespread use of X-ray technologies, they are still inefficient when it comes to mobile scenarios. In addition, their detection capability is based solely on visual imaging with no way to examine the exact material content of small shipments.
We are proposing a completely novel, yet highly effective solution for this. The CosmoPort project will develop the next generation of scanner systems using Atmospheric Ray Tomography (ART). Such systems are equipped with advanced Machine Learning-based risk assessment tool. As muon tomography itself is a technique already in use, then we will develop the first mobile solution combined with AI/ML tools to enhance material and object classification for the first time ever.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-IA - HORIZON Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-CL3-2022-BM-01

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Coordinator

GSCAN OU
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 294 825,00
Address
MUSTAMAE LINNAOSA, MAEALUSE TN 2/1,
12618 Tallinn
Estonia

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Eesti Eesti Põhja-Eesti
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 849 750,00

Participants (7)

Partners (2)

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