nature of X-ray security imagery in terms of the variation of objects present, object orientation, inter-object occlusion and complex concealment by adversaries continue to present a range of challenges for effective and robust automated screening approaches. This project aims to address these challenges by leveraging a range of recent advances in aligned domains spanning both computer vision and deep machine learning research.

There are a wide range of potential research directions, that could form the basis for a specific PhD project in this area:

• material and appearance based anomaly detection
• multi-view object detection and classification
• adaptation of AI foundational models, including visual-language models (LLM/VLM)
• occluded and disassembled object detection
• transparent object segmentation and extraction
• category discovery and out of distribution detection
• complex, multi-part object detection

nature of X-ray security imagery in terms of the variation of objects present, object orientation, inter-object occlusion and complex concealment by adversaries continue to present a range of challenges for effective and robust automated screening approaches. This project aims to address these challenges by leveraging a range of recent advances in aligned domains spanning both computer vision and deep machine learning research.

There are a wide range of potential research directions, that could form the basis for a specific PhD project in this area:

• material and appearance based anomaly detection
• multi-view object detection and classification
• adaptation of AI foundational models, including visual-language models (LLM/VLM)
• occluded and disassembled object detection
• transparent object segmentation and extraction
• category discovery and out of distribution detection
• complex, multi-part object detection