Real-time stereo wave imaging on moving vessels for seakeeping
PhDs and postgraduate research
Funded PhD Project (UK and EU students only)
School of Mechanical and Design Engineering
4 May 2021 (12pm GMT)
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.
The work on this project will:
- Compare and evaluate existing stereo-vision pipelines of 1) epipolar semi-global search method, and 2) variational surface shape model based dense 3D reconstruction of moving wave
- Develop hydrodynamic wave models to simulate in detail wave-induced load on the vessel
- Modify the variational method to a sparse surface wave model by incorporating a statistical model of hydrodynamics in the optimisation functionals for matching to allow real time estimation of wave propagation parameters
- Apply computer vision techniques of superpixel and image abstraction, and machine learning methods to further reduce processing time of wave semantic feature extraction
The project intends to develop a stereo-vision based feature extraction pipeline of oncoming waves at close-proximity. The machine vision system will be deployed on fast marine vessels as a key part of seakeeping and shock mitigation controller for both manned and unmanned vessels that considers the well-being of human occupants. The outcome will devise understanding and correlation between close-range wave characteristics with dynamic loads transmitted to the vessel structure and humans.
The School of Mechanical and Design Engineering owns two small-scale unmanned surface test vessels with FLIR® stereo vision camera system dedicated to this project for wave data collection and machine vision algorithm validation. The project will start with comparing and adapting existing stereo vision techniques to achieve off-line wave characterisation. The new in-the-loop feature detector and the unique close-wave imagery data made available will make a significant contribution to the wider machine vision community for outdoor mobile systems and the marine industry.
The visual features will form an important part of the seakeeping and shock-mitigating navigational decisions that are key to the future surface vessels for safer operations for personnel and casualties. The project is aligned with the University’s vision to build global and national partnership through the boundary-breaking themes of future transportation and intelligent systems.
The student is expected to collaborate with project partners from areas of visual computing, hydrodynamic modelling, biomechanics and industrial partners in the maritime industry. Furthermore, the student is expected to attend multiple events such as conferences, project meetings, and workshops
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in computing, engineering, or physics 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.
Candidates should hold an undergraduate first class degree or Master’s with distinction (or non-UK equivalent) in Robotics, Computer Science, Electronics, Mechanics, Mathematics, Physics or a similar discipline. Experience of programming, image processing, computer graphics, machine vision, and deep learning is desirable.
How to apply
We’d encourage you to contact Dr Ya Huang (email@example.com) 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 funded PhD opportunity you must quote project code SMDE6040521 when applying.