Study level

Vacation research experience scheme

Faculty/Lead unit

Science and Engineering Faculty

School of Electrical Engineering and Robotics

Topic status

We're looking for students to study this topic.

Supervisors

Dr Simon Denman
Position
Senior Lecturer
Division / Faculty
Science and Engineering Faculty
Associate Professor Grant Hamilton
Position
Associate Professor in Ecology
Division / Faculty
Science and Engineering Faculty

Overview

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.

While it's highly desirable to use drones and aerial footage to monitor wildlife, there are substantial challenges created by the nature of the data and target wildlife.

This, combined with the vast nature of any collected aerial data, makes manual analysis difficult. This challenge motivates the development of machine learning methods to automatically process data and perform tasks, such as:

  • detecting target animals
  • counting herd animals
  • classifying land use
  • assessing environment health.

Research activities

As part of the research project, you will:

  • investigate and deploy machine learning algorithms for tasks including object detection, object counting and scene segmentation
  • investigate improvements to the deployed algorithms to enhance performance
  • benchmark developed algorithms and assess accuracy/performance.
Exact task details will be determined in consultation with the project supervisors.

Outcomes

The outcomes of this work will include:

  • one or more algorithms that are trained and deployed on aerial wildlife data
  • performance benchmarks for the deployed algorithm(s).

Skills and experience

Strong programming experience, especially with Python, is ideal.

Some prior machine learning or computer vision experience is desirable, but not mandatory.

Keywords

Contact

Contact the supervisor Dr Simon Denman for more information.