Deep Learning-Based Smart Autonomous Systems for the Visual Inspection of Asset Integrity in Harsh Environment

Robotic and autonomous systems (RAS) have received the increasing interests both in onshore and offshore applications where harsh environment has been a challenging issue particularly in the oil and gas industry. With the rising safety, time and cost concerns relating to the inspection of important industrial equipment and infrastructures, the use of small, lightweight and smart RASs such as UAVs/drones that can be deployed quickly to assess the internal and external conditions are highly desirable.

This PhD project aims to fundamentally investigate novel RAS solutions to the autonomous visual inspection and assessment of internal and external surface conditions through machine learning, computer vision, control theory, and artificial intelligence, etc.

This is the Strathclyde's international fees only studentship for 3 years up to £60,000.