Manufacturing industries contemplate integrating computer vision and artificial intelligence into shop
floor operations, such as steel surface defect identification, to realize smart manufacturing goals.
However, inadequate annotated training datasets and reduced prediction abilities with image perturbations
restrict the practical implementation. This paper introduces NSLNet framework utilizing ImageNet
as a feature-extractor combined with adversarial training in the extracted feature space through Neural
Structure Learning to address these barriers. The experiments on public (NEU) and synthetically generated
datasets (ENEU) showed that the NSLNet could learn with few training samples maintaining resilience
against image perturbations outperforming conventional models significantly and nearest deep
learning competitors marginally.