Unconstrained Object Understanding using Deep Learning for Computer Vision in X-ray Security Screening

About the project

About the Project X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in future automated screening systems that can be deployed for transportation and border security screening alike. Within this context deep learning based computer vision techniques have already been used to enable automated prohibited and threat item detection spanning X-ray scanned baggage, freight and postal items. However, the complexity, variety and unconstrained 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 Supporting Resources The Department of Computer Science at Durham University hosts well-equipped labs with on-site capabilities comprising vehicle-mounted sensors (LiDAR, radar, camera, GPS/IMU), drone operations, on/off-road robotics, high-precision geo-localisation, BCI/EEG bio-signal data collection, robotic arm manipulation, X-ray security imaging, virtual reality and on-demand wide-area surveillance video feeds. In addition Durham University hosts the UK regional supercomputer, Bede (128 NVIDIA V100 GPUs) which complements our departmental NVIDIA CUDA Compute Cluster (80+ GPUs up to NVIDIA A100) to cater for the increasing GPU compute demands of modern AI-driven research projects. The department itself is based in the newly built Mathematical and Computer Sciences building - a £42 million, 9,160m 2 state of the art teaching, learning and research facility, located on the University's Upper Mountjoy Campus co-located with the Department of Mathematics. All PhD students are allocated desk and/or lab space to support their research work and PhD student research projects are additionally supported by an annual equipment/travel allowance spanning the duration of the PhD study period. Supervision You will be supervised by Prof. Toby Breckon ( https://breckon.org/toby/ ) , Professor of Computer Vision and Image Processing in both the Department of Computer Science and Department of Engineering, and Head of Visual Computing (Computer Science) at Durham University in collaboration with one or more staff from the VIViD research group . The work of Prof. Breckon's research team relates to all aspects of computer vision and robotic sensing – the automatic understanding of images by computer as an aspect of artificial intelligence using deep learning (i.e. "visual AI" ). Within this domain his team specializes in several industry-facing problem domains spanning X-ray image understanding , automotive vision (autonomous vehicles), visual surveillance , robotic sensing and general topics in object detection/classification . This has resulted in over £29+ million of research income (to 2026+), collaboration with 40+ government and industry partners, over 200 research publications and supported the development of AI software start-up COSMONiO by former team members (acquired by Intel, 2020). During the PhD study, you will receive extensive training and research guidance via regular one-to-one supervision meetings with Prof. Breckon, in addition to weekly research team meetings, to allow you to both develop your research potential and consolidate your technical skill-base. Prof. Breckons’s research team has a diverse and collaborative culture that aims to facilitate the realisation of each individual's abilities against a range of available research opportunities. Previous PhDs from the team have published their research in a range of prestigious venues (e.g. CVPR, ICCV, ECCV, BMVC) and graduated to a range of careers in the AI industry . Durham University Durham University is a world top-100 university (QS), a European top-50 university (QS), ranked the 3rd in the UK (Times, 2026) and a member of the research-intensive Russell Group of UK universities, focussing on research excellence delivered by world-leading academics. Durham is the third oldest university in England, following Oxford and Cambridge, founded in 1832 and situated in the ancient City of Durham, a relatively small city by international standards (pop. ~50,000) situated in the North East of England with long-established historical roots (including an on-campus UNESCO world heritage site comprising Durham Cathedral and Castle). Today the university offers a strong and unique collegiate student experience, with each student being a member of one of the university's 17 constituent colleges, with a relatively low cost of living compared to other parts of the UK . Entry Requirements First class honours (or high 2.1 at undergraduate or equivalent Masters) degree in an Engineering, Physics, Maths or Computer Science based subject (internationally: GPA 3.3+ or equiv). Strong understanding of computing/engineering applications and mathematical problem solving. Knowledge of modern programming languages (ideally including one of Python, C++/C or Rust). Excellent written and spoken communication skills in English, meet the Durham University English language requirements ( https://www.dur.ac.uk/study/international/entry-requirements/english-language-requirements/ ). Prior experience in research, industry and/or AI based research topics is beneficial but not essential. How to Apply Please send an email with your CV, degree transcripts and any supporting documents to Professor Toby Breckon toby.breckon@durham.ac.uk for an initial pre-application discussion.

Research areas