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.

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.