Automatic prohibited item detection, using computer vision techniques on 2D X-ray and 3D CT imagery.
Durham Computer Science research on automatic and algorithmic prohibited item detection, using a range of computer vision techniques on both 2D X-ray and 3D Computed Tomography (CT) imagery, has directly informed UK/US government aviation security policy.
This has provided new enhanced software capabilities for X-ray security scanners across 8 companies who supply the aviation and border security sector.
Our work now directly contributes to the security of over 500 million passenger journeys per annum across five continents, with technology from Durham now available at an ever-increasing number of major international airports. The technology has commercial reach to 2-3 billion passenger journeys across 30+ countries globally, and will now help secure all air passengers attending the 2022 FIFA World Cup in Qatar.
A reference architecture for plausible Threat Image Projection (TIP) within 3D X-ray computed tomography volumes.
On the impact of object and sub-component level segmentation strategies for supervised anomaly detection within x-ray security imagery.
Using deep Convolutional Neural Network architectures for object classification and detection within X-ray baggage security imagery.
On using feature descriptors as visual words for object detection within x-ray baggage security screening.
Contact: Toby Breckon
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