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Our Vision for Impact

Our aim is to have impact creation embedded within all aspects of our research, to promote a strong impact culture, a have a diverse portfolio of impact activities and to many staff involved in impact creation.

 Our strategies are:

  • promote a strong impact culture and develop and enable impact appreciation and awareness across the Department
  • provide leadership and co-ordination for the Department's external engagement and impact activities and strategically manage the development, dissemination, support and sustainability of departmental impact
  • encourage and enhance the development of cross-disciplinary links with other academic disciplines, knowledge transfer and public engagement so as to build pathways to impact.

Impact Case Studies

 A selection of recent and ongoing impact generation projects that are underpinned by Computer Science research follow.

MammalWeb

MammalWeb (www.mammalweb.org) is a citizen-science project established by researchers at Durham University to monitor the UK’s mammals.  Camera traps are deployed by members of the public to capture images of wildlife and the resultant images are uploaded to the MammalWeb platform where they are then classified online. MammalWeb’s objectives are to:

 deliver policy-relevant data on biodiversity to the regional and national records centres in the UK

  • demonstrate the wider potential of a citizen-based model for mammal monitoring
  • engage members of the public in the wildlife around them.

 The project has collaborated with a range of partners including Durham Wildlife Trust, Great North Museum Hancock, Scottish National Heritage, NatureSpy, British Trust for Ornithology and HMP Deerbolt.

 Contact: Steven Bradley

Object Detection, Classification, Localization and Tracking for Automated Wide-Area Surveillance

Durham Computer Science research on computer vision algorithms enables automated image understanding to provide long-term wide-area surveillance of dynamic scene objects (e.g. people, vehicles) addressing questions such as: “Is there anything there?” (detection); “What is it?” (classification); “Where is it?” (localization); and “What is it’s behaviour?” (tracking). This research, as part the SAPIENT programme, informs scientific work by the governments of UK, USA, Canada, Australia, New Zealand and Netherlands on wide-area, multi-sensor surveillance systems. Our research has contributed to £23.2 million investment in multi-sensor surveillance systems (UK/US government/industry), £11.3 million of additional commercial income to UK companies and supported the creation of around 55 additional science and engineering jobs across six organisations.

Contact: Toby Breckon

Image Understanding and Analysis for Next Generation Airport Security Scanners

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 and 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 2021 FIFA World Cup in Qatar.

Contact: Toby Breckon

Sensing for Future Autonomous Vehicles

Durham's Computer Science research on the use of automated image understanding techniques for future autonomous vehicles (driverless cars) addresses the two key algorithmic tasks within on-vehicle scene understanding: “Where am I?” (known as localization); and “What is around me?” (known as semantic scene understanding). The key challenge is to be able to address these tasks accurately, efficiently (i.e., in real-time relative to the vehicle speed) and robustly under varying environmental (weather) conditions. Our research in this area has directly informed the research and development at two of Europe's leading automotive manufacturers and supported the translation of road vehicle localization technology into rail where it now helps to protect 4.3 billion passenger journeys annually over around 57,000 km of track (Germany/UK).

Contact: Toby Breckon

Using Machine Learning in Visual Art

In a project funded by the AHRC's Towards a National Collection program, Leonardo Impett from Durham Computer Science and Joasia Krysa from Liverpool John Moores University are working on machine-learning-powered curation in the 2020-21 Liverpool Biennial. The project looks at how the public interact differently with visual art events than they would with physical ones. In particular, it looks at how interaction might change when visitors no longer passively watch an event but actively participate in its curation. In an online edition of the 2020-21 Liverpool Biennial, visitors will co-curate the event with a machine learning algorithm, taking existing techniques beyond the “search engine” context in which they have mostly been used to date.

 Contact: Leonardo Impett

Reducing the Ionising Radiation Exposure due to CT Scans

Computer Science has a collaborative project with the NHS University Hospital of North Durham to reduce the ionising radiation exposure of CT scans through utilising deep neural networks. CT scans are expensive in terms of costs and availability, whereas deep generative neural networks are capable of rapidly reconstructing high-quality 3D CT-like images. The success and scalability of generating CT-like images from a small amount of 2D X-ray radiation exposure will have a significant impact on patients (thanks to reduced radiation exposure) and hospitals due to the prohibitively expensive nature of CT scans. Practitioners are obliged by law to consider available alternative techniques which have the same objective but expose patients to less ionising radiation.

Contact: Chris Willcocks

Alexandra Cristea / Sue Black

TechUpWomen is a Durham led initiative of four UK universities (with York, Nottingham and Edge Hill) for cross-training women into technology careers. Priority is given to those from the BAME (54%) and LGBTQ+ (21%) communities and to those with disabilities (46%) or dependants (40%). In 2020, the first cohort of 100 women successfully completed a six month online programme, developed in collaboration with industry, in preparation for roles such as software developer, data scientist, agile project manager and business analyst. Our graduates have found new roles or promotions in a wide range of industries, including manufacturing (Jaguar Land Rover and MSP), software (Double Eleven Ltd), education (JISC and Code Nation), service (HR in One) and the public sector (Newcastle City Council and Durham Constabulary). The initiative won the Employment and Skills category in the UK Impact Awards 2020.

 Contact: Sue Black and Alexandra Cristea