  Nondestructive evaluation methods play an important role in ensuring
component integrity and safety in many industries. Operator fatigue can play a
critical role in the reliability of such methods. This is important for
inspecting high value assets or assets with a high consequence of failure, such
as aerospace and nuclear components. Recent advances in convolution neural
networks can support and automate these inspection efforts. This paper proposes
using residual neural networks (ResNets) for real-time detection of corrosion,
including iron oxide discoloration, pitting and stress corrosion cracking, in
dry storage stainless steel canisters housing used nuclear fuel. The proposed
approach crops nuclear canister images into smaller tiles, trains a ResNet on
these tiles, and classifies images as corroded or intact using the per-image
count of tiles predicted as corroded by the ResNet. The results demonstrate
that such a deep learning approach allows to detect the locus of corrosion via
smaller tiles, and at the same time to infer with high accuracy whether an
image comes from a corroded canister. Thereby, the proposed approach holds
promise to automate and speed up nuclear fuel canister inspections, to minimize
inspection costs, and to partially replace human-conducted onsite inspections,
thus reducing radiation doses to personnel.
