Current industry practice for monitoring internal pipeline corrosion is to visually compare the most recent radiographic image of a pipeline to previous radiographic images from the same location. To improve the inspection quality and efficiency, this project aims to create an application that automates the detection and assessment of internal pipeline corrosion. Through image processing techniques, the final application automates the existing inspection process.
Team Members:
Enrique Callado
Kendal Clovis
Aaron Li
Nick Pacheco
Sponsors
BP
Semester