The primary goal of SMAUG is to improve the underwater detection of threats in ports and their entrance routes, by means of a integrated system capable of providing data concerning threat analysis between 3 main elements: ports security infrastructure, advanced underwater detection systems and surveillance vessels. Underwater detection and location will be performed by four primary methods: i) acoustic detection, where a series of hydrophones will listen for sounds emitted by small underwater vehicles and will be processed by artificial intelligence methods, ii) rapid sonar hull scan, used to scan ships hulls and perform harbour floor scanning, iii) high resolution sonar inspection, to inspect objects in water with poor visibility and iv) collective autonomous location, where a swarm of autonomous underwater vehicles will act cooperatively. This will provide information to Artificial Intelligence modules which will improve the way detecting illicit and dangerous goods and/or of threats hidden below the water surface is currently done, taking into account sources such as Unmanned Surface Vehicle Systems, (USV), underswater remote operation vehicle (ROV), UAV (Aerial autonomous vehicle) and Port current information sources. The combination of these tools will allow SMAUG to prompt solutions capable of detecting possible threats to infrastructure or vessels, as well as identify vessels with concealed goods.

EU customs authorities are a cornerstone in the international trade landscape, playing a critical role in preventing the entry of illegal goods, safeguarding revenue, & ensuring the seamless flow of goods. Balancing these responsibilities is particularly challenging in today’s environment, where the volume of global trade continues to expand rapidly. Mitigating the entrance of illegal goods is becoming increasingly difficult, especially with limited human resources. Aiming to address these challenges, the CustomAI consortium has united its expertise and competences to develop an AI-toolkit that will reduce the number of false positives (situations where the cargoes like shipping containers or parcels have been selected for inspection despite not containing contraband). The proposed AI-toolkit will revolutionise customs operations by involving non-intrusive and robust AI-enhanced technologies for predicting, detecting, and selecting high-risk cargoes for inspection. The VCCO concept is adopted for managing all processes in the customs control of artefacts (e.g. container, parcel). Key components of the AI toolkit include: * AI-based risk anticipation relying on AI-analysis of internal knowledge in compilation with external multilingual data, including manifest and declarations. Only relevant cargos will be sent for inspection. * AI-enhanced vapour-based detectors implied only on the containers selected in the previous step. * AI-based x-ray for threat detection in containers applied on output of step two (the human inspection takes place only after this step). * Multimodal LLM Continual Learning model, which will have as input, x-ray and camera images, and will be trained on threat dataset composed of threat samples (x-ray and visual images of threat parcels) updated by customs. * Blockchain technology for secure data sharing & supply chain traceability. By adopting these cutting-edge technologies, the CustomAI toolkit is set to revolutionise customs operations.

Safe and legal global transport is essential to human welfare, but it demands that customs authorities implement security regulations with a special focus on illicit goods. To enable enforcement of regulations tackling illicit goods trafficking, customs authorities have expressed their need for portable, affordable, non-intrusive, reliable screening technologies aiming to facilitate on-site and rapid inspections. The current technologies are mainly based on X-ray screening, but have its limitations in detecting illicit goods, are very expensive and the process is time consuming. This makes that currently less than 5% of the containers may be inspected, which is undesirable for customs authorities and society. In the METEOR project, the consortium will develop a prototype of a portable and versatile air sampling-based screening system. This will enable customs administrations worldwide to rapidly and reliably inspect for the presence of illicit goods. The METEOR technology will provide a new concept of cargo screening detector with a highly efficient air sampling technique and the ion mobility multidetector sensing technology. The METEOR analyser relies on a very innovative concept of a multi-detector differential mobility analyser (DMA), that classifies molecular ions based upon their electrical mobility. This generates a chemical fingerprint of the samples, and after the processing with non-targeted screening techniques, it is possible to accurately classify and detect the threats. The focus will be on the detection of illicit drugs, but also explosives and other substances. The goal is to develop the METEOR technology up to TRL7, and validate it in the operational environment. This is done by the four customs administrations (The Netherlands, Belgium, Spain and Ireland) involved in the project, that cover some of the main ports in Europe.

Everyday our customs workers need to tackle counterfeit goods and piracy to protect the health and safety of our citizens, yet it is estimated that only a small fraction of cargo is inspected and even smaller fraction of illegal goods are detected. Today, the most widely used technology for scanning vehicles, ranging from vans and trucks to railcars, is gamma-ray and X-ray radiography. But new technologies are required for overcoming current technological shortcomings like inability to detect the materials, usage of radioactive and harmful source, low throughput to name some. Cosmic-ray tomography (CRT) is considered as beyond the state-of-the-art technology in cargo screening. Cosmic-ray muons are highly penetrating, their average energy is about 10,000 times the energy of a typical X-ray and they are practically non-absorbable. They are suitable to identify materials hidden inside of shielded material, too thick or deep for other imaging methods. The CRT is completely passive, exploiting naturally occurring secondary cosmic radiation. Contrary to conventional X-ray or gamma-based imaging techniques it allows to distinguish between different materials and localizes it inside the cargo or vehicle by providing visualised 3D image. We will bridge the major security gap for fast and safe inspection of large number of cargos by developing the Multi-Functional Passive Detection System. The detection capability is based on using high accuracy sensors for particle tracking in combination with beyond state-of-the art tomographic reconstruction and material classification algorithms. The main objective of SilentBorder is to develop and validate a new high-technology CRT scanner for border guard, customs and LEAs that enables safe and fast screening, detection and identification of hazardous and illegal goods (e.g. SNM), contraband (e.g. tobacco or explosive) as well as hidden persons in up to 20’ iso containers.

The BAG-INTEL project will provide robust AI based information utilization and decision support tools, within the context of advanced detection systems to support customs for increased effectiveness and efficiency of the customs control of air traveller baggage in inland border airports, while minimizing the human customs resources needed. This aim addresses the challenge of maintaining effective and efficient customs control of passenger baggage in the situation of the substantial growth of the volume of air travellers arriving in inland border airports with the limited human customs resources available. For this aim, the project will develop an integrated system solution comprising: (1) new AI powered functionality for enhanced detection of contraband in x-ray scanning of luggage, (2) AI camera based end-to-end reidentification of luggage, (3) digital twin for system visualisation and performance optimization for the operational context of an airport, (4) use case for test demonstration and evaluation in 3 European airports, a small, a medium sized, and a big airport, and (5) wide dissemination and elaboration of easy-to-use training material for end users. For the customs, BAG-INTEL solution aims to: increase the successful detection of contraband in luggage by at least 20%; demonstrate the possibility and utility in automatically to derive risk indicators from external data such as the Advanced Passenger Information; demonstrate the effectivity of AI camera based reidentification of luggage, when the traveller carries it into the customs space at the exit of the carousel area; increase the fluidity of passenger flow and control by at least 20%; decrease the customs personal resources mobilisation by at least 20%; derive data useful in flights risk assessment; derive data useful in flights risk assessment; demonstrate the autolearning capacity of this smart risk engine.