Learning to Adapt Deep Corrosion Assessment Models From Indoor to Outdoor Image Domains

Corrosion of materials impacts critical economic sectors from infrastructure to transportation. The development of safe, corrosion-inhibiting materials is thus an important area of study in materials science. Traditional corrosion science, preparing and monitoring materials under adverse conditions, is labor intensive and extremely costly. While deep learning has become popular in automating engineering tasks, the development of deep models for corrosion assessment is lacking. We study the unique problem of deep domain adaptation (DA) for automated corrosion assessment of corrosion-inhibiting materials. Corrosion data, i.e., photographic images of corroding materials, is abundant when produced in an artificially controlled laboratory, while controlled images from natural outdoor environments are limited. We thus leverage the more readily available artificial-environment indoor data to train a corrosion assessment classifier to transfer it via domain adaptation. In doing so, we can perform well on the smaller, yet more realistic, outdoor corrosion data, without requiring target labels. We empirically evaluate 5 popular DA models on real-world corrosion image data. Further, we design 8 strategies for ensembling these models. Our study finds across evaluation metrics of accuracy, F1-score, and balanced accuracy that ensembled models incorporating both predictions and confidence scores from each DA model outperform individual DA models. They achieve 41% relative improvement in test accuracy compared to a no-DA baseline. Additionally, we perform a failure analysis study of our model to explain its performance.