Deep Learning for Habitat Recognition

Deep Learning for Automatic Marine Habitat Recognition by Intelligent Underwater Video Systems

Degree type

MRes/PhD

Closing date

10 October 2022

Campus

Hobart

Citizenship requirement

Domestic/International

About the research project

The southern coast of Australia has been identified as a climate change hotspot, with the increased poleward penetration of the East Australian Current accelerating the warming rate. An implication of this change is the range-extension of several species, including the long-spined sea urchin's arrival to the Tasmanian East coast. This sea urchin can overgraze kelp forests, turning healthy habitats into barren grounds. Significant changes have been observed in the structure and functioning of the Tasmanian reef system because of its invasion, which has also impacted the valuable rock lobster and abalone fisheries.

Underwater imagery has proven to be a powerful tool for monitoring benthic communities and habitats. Hence, a camera system was developed for this purpose, the so-called POTBot (Picture Of The Bottom), which may be mounted on rock lobsters pots. POTBot units are equipped with a camera system, including underwater sensors, to record videos and georeferenced environmental data. The units deployed by lobster fishers provide an opportunity for state-wide information collection for monitoring spatial and temporal changes of conspicuous marine species and habitats.        

Underwater images have been typically human analysed, which is time-consuming, costly, and unsuitable for ongoing programs. Hence, Artificial Intelligence arises as a solution. For instance, Deep Learning (DL) approaches have been successful in automatic underwater object identification. This type of algorithm includes different architectures of Deep Neural Networks (DNN), already used for aquatic object identification. Still, challenges remain unresolved in image recognition of aquatic environments. Such matters include water turbidity, insufficient light, complex background, morphology of organisms, etc. Thus, the challenge is to address the complexity of aquatic environments by using optimal DNN techniques to improve the efficiency of marine habitat recognition. This project aims to employ currently available and develop new Deep Learning algorithms to recognise conspicuous species and habitats and improve ongoing monitoring.

Primary Supervisor

Meet Dr Shuxiang Xu

Funding

Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:

  • a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 3.5 years
  • a relocation allowance of up to $2,000
  • a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)

If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.

As part of the application process you may indicate if you do not wish to be considered for scholarship funding.

Eligibility

Applicants should review the Higher Degree by Research minimum entry requirements.

Additional eligibility criteria specific to this project/scholarship:

  • Applications are open to Domestic/ International/ Onshore applicants
  • Applicants must be able to undertake the project on-campus

Selection Criteria

The project is competitively assessed and awarded.  Selection is based on academic merit and suitability to the project as determined by the College.

Application process

There is a three-step application process:

  1. Select your project, and check you meet the eligibility and selection criteria;
  2. Contact the Primary Supervisor, Dr Shuxiang Xu to discuss your suitability and the project's requirements; and
  3. Submit an application by the closing date listed above.
    • Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
    • As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.

Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.

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