  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 artificial
intelligence (A.I.). The training of a deep learning model requires intensive
image labelling, and in order to generate a large database of labelled images,
crowd sourced labelling via a dedicated website was sought. The website
(corrosiondetector.com) permits any user to label images, with such labelling
then contributing to the training of a cloud based A.I. 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 crowd
sourced training process, but also the end use of the evolving model. Herein,
the results and findings from the website (corrosiondetector.com) over the
period of approximately one month, are reported.
