Self-Learning for Computer Vision Asset Management
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
Applications accepted all year round
The work on this project could involve:
- Adaptive and self-learning Neural Network structures
- Faster R-CNN, RetinaNet, YOLO.
- Multi-Sensor input for self-learning
Artificial neural networks are widely used to undertake complex image analysis, and carry out tasks such as identifying objects in a noisy environment. Typically Neural Networks will be trained in advance. Performance of the system is hence dependent on the quality and amount of training.
Artificial neural networks require vast quantities of accurately labelled training data, e.g. thousands of images with objects accurately defined. This is a time consuming and costly
step. In many situations environmental influences such as perspective, reflections and ambient lighting will affect how the system performs.
This project will investigate ways of allowing Neural Network Systems to apply self-learning post deployment and hence continue to improve performance past the initial training stage. Input from multiple sensors will be explored and how this sensor collaboration can improve the self-learning aspect for asset management applications with multi-sensor coverage.
The work will be undertaken in the University of Portsmouth, as part of a multi-disciplinary team of Engineers and Computer Scientists keen to explore new avenues for the use of
Artificial Neural Networks in everyday life.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2020/2021 entry (for October 2020 and February 2021 entries)
Home/EU/CI full-time students: £4,407 p/a
Home/EU/CI part-time students: £2,204 p/a
International full-time students: £16,400 p/a
International part-time students: £8,200 p/a
PhD by Publication
External candidates £4,407 p/a
Members of staff £1,680 p/a*
2021/2022 entry (for October 2021 and February 2022 entries)
PhD and MPhil
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £17,600 p/a
International part-time students: £8,800 p/a
All fees are subject to annual increase.
PhD by Publication
External Candidates £4,407 p/a*
Members of Staff £1,720 p/a*
If you are an EU student starting a programme in 2021/22 please visit this page.
*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021.
Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.
You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
How to apply
We’d encourage you to contact Dr I.kagalidis (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this PhD opportunity you must quote project code COMP5020220 when applying.