Computer Vision for The Energy Sector

Topics

On completion of this module, students are expected to be able to:

1. Critically appraise the challenges posed by the management and processing of complex image and/or video-based datasets.
2. Demonstrate an understanding of the main concepts of computer vision and how machines "see".
3. Critically evaluate and select state-of-the-art methods to extract features from the input data and detect, localise, recognise or classify the information or phenomena depicted.
4. Discuss solutions to diverse case studies from real-life applications in the Oil & Gas and renewable sectors.

The indicative content covered in this course includes:

• Main concepts of computer vision
• Data in the energy sector
• Data acquisition and storage
• Data pre-processing and cleaning
• Machine learning principles
• Image manipulation
• Basic and advanced models for image classification, detection, recognition and segmentation
• Real-life use cases
• Tools and libraries (e.g. Python, OpenCV, Scikit Learn, cloud services, etc.)
• Sharing code and working remotely & effectively (online notebooks and repositories e.g. GitHub, Kaggle, Colab, etc.).

Teaching

Teaching/learning activity as follows:

• Live Lectures: 1 hour/week
• Live practical sessions for tutorial exercises: 2 hours/week
• Tutorial exercises: a range of guided exercises to help participants further explore the principles covered in lectures.

Assessment

• A project applying techniques of computer vision to a dataset and presenting the analysis and conclusions in the form of an interactive report with code (Jupyter Notebook).

Independent Study

• Materials and exercises are available online, allowing participants to study flexibly and independently at time and place to fit around existing work and life commitments.
• Further reading resources.
• Online tutor support.

Staff Delivering on This Course

The course team is comprised of experienced academics who won multiple STAR awards and have worked on multiple research and consultancy projects in the field of machine learning and computer vision. Guest lectures showcasing real-life success stories will be delivered by industry partners.

Academic Support

The Inclusion Centre advises and supports students who disclose a sensory or mobility impairment, chronic medical condition, mental health issue, dyslexia and other specific learning differences. Applicants are encouraged to arrange a pre-entry visit to discuss any concerns and to view the facilities.

Online Learning & Support

All online learning students, benefit from using our collaborative virtual learning environment, CampusMoodle. You will be provided with 24/7 online access to your learning material and resources, along with the ability to interact with your class members and tutors for discussion and support.

Study Skills Support

The Study Support Team provides training and support to all students in:

• Academic writing
• Study skills (note taking, exam techniques, time management, presentation)
• Maths and statistics
• English language
• Information technology support

Library Support

The Library offers support for your course, including the books, eBooks, and journals you will need. We also offer online reading lists for many modules, workshops and drop-ins on searching skills and referencing, and much more.