Deep learning is revolutionising the way that many industries operate, providing a powerful method to interpret large quantities of data automatically and relatively quickly. Deterioration is often multi-factorial and difficult to model deterministically due to limits in measurability, or unknown variables. Deploying deep learning tools to the field of materials degradation should be a natural fit. In this paper, we review the current research into deep learning for detection, modelling and planning for material deterioration. Driving such research are factors such as budget reductions, increasing safety and increasing detection reliability. Based on the available literature, researchers are making headway, but several challenges remain, not least of which is the development of large training data sets and the computational intensity of many of these deep learning models.

Corrosion costs an estimated 3–4% of GDP for most nations each year, leading to significant loss of assets. Research regarding automatic corrosion detection is ongoing, with recent progress leveraging advances in deep learning. Studies are hindered however, by the lack of a publicly available dataset. Thus, corrosion detection models use locally produced datasets suitable for the immediate conditions, but are unable to produce generalized models for corrosion detection. The corrosion detection model algorithms will output a considerable number of false positives and false negatives when challenged in the field. In this paper, we present a deep learning corrosion detector that performs pixel-level segmentation of corrosion. Moreover, three Bayesian variants are presented that provide uncertainty estimates depicting the confidence levels at each pixel, to better inform decision makers. Experiments were performed on a freshly collected dataset consisting of 225 images, discussed and validated herein.

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.