The UK Border Force is aiming to expand its use of AI to enhance the searching and screening of freight at UK seaports.

In order to balance security with the smooth movement of legitimate goods, the agency is seeking to automate the detection of anomalies in x-ray images, as manual analysis is both time-consuming and prone to error.

It is hoped that AI-driven automation will speed up screening times and also increase the volume of goods processed. It will also free up officers to focus on critical tasks, reduce false alarms and minimise unnecessary secondary inspections. 

The Accelerated Capability Environment (ACE) supported the Border Force in organising and analysing its extensive x-ray image database, which had previously been stored inconsistently across multiple data structures.

ACE was initially tasked with creating a fully indexed and standardised repository of x-ray images and associated data, making it easily accessible to analysts and suitable for AI algorithm development.

Six ACE suppliers collaborated to explore innovative AI and machine-learning methodologies for anomaly detection. Using the newly indexed data, they developed three use cases: vector integrity, pattern recognition and high-density material detection.  

The insights were then presented to Border Force and the solutions will be taken forward for trials in a controlled operational environment.