ABSTRACT
This study investigates and test solutions for automated corrosion detection processes that focus on the visual attributes of corrosion. Corroded surfaces have two visually identified attributes color and texture. To detect corrosion based on color, a color tracking algorithm is created and tested using images from different compartments of vessels. To detect corrosion based on texture, deep learning algorithms are used, and two approaches are tested. The first approach is a binary classification model trained using a Convolutional Neural Network (CNN) architecture employing transfer learning. The model is used by a sliding algorithm to allow detection and localization in large metallic plates containing both corroded and clean regions. The second approach treats corrosion detection as an object detection problem. A Single Shot Detector (SSD) is trained using transfer learning to detect corrosion on real world inspection images of bulk carriers. The study finds all three methods capable to detect corrosion with the deep learning approaches yielding better results.
