Automated Corrosion Detection Using Crowdsourced Training for Deep Learning

Abstract

The automated detection of corrosion from images (i.e., photographs) or video (i.e., drone footage) presents significant advantages in terms of corrosion monitoring. Such advantages include access to remote locations, mitigation of risk to inspectors, cost savings, and monitoring speed. The automated detection of corrosion requires deep learning to approach human level intelligence. Training of a deep learning model requires intensive image labeling, and in order to generate a large database of labeled images, crowdsourced labeling via a dedicated website was sought. The website (corrosiondetector.com) permits any user to label images, with such labeling then contributing to the training of a cloud-based artificial intelligence (AI) model—with such a cloud-based model then capable of assessing any fresh (or uploaded) image for the presence of corrosion. In other words, the website includes both the crowdsourced training process, but also the end use of the evolving model. Herein, the results and findings from the Corrosion Detector website, over the period of approximately one month, are reported.