  For steel product manufacturing in indoor factories, steel defect detection
is important for quality control. For example, a steel sheet is extremely
delicate, and must be accurately inspected. However, to maintain the painted
steel parts of the infrastructure around a severe outdoor environment,
corrosion detection is critical for predictive maintenance. In this paper, we
propose a general-purpose application for steel anomaly detection that consists
of the following four components. The first, a learner, is a unit image
classification network to determine whether the region of interest or
background has been recognised, after dividing the original large sized image
into 256 square unit images. The second, an extractor, is a discriminator
feature encoder based on a pre-trained steel generator with a patch generative
adversarial network discriminator(GAN). The third, an anomaly detector, is a
one-class support vector machine(SVM) to predict the anomaly score using the
discriminator feature. The fourth, an indicator, is an anomalous probability
map used to visually explain the anomalous features. Furthermore, we
demonstrated our method through the inspection of steel sheet defects with
13,774 unit images using high-speed cameras, and painted steel corrosion with
19,766 unit images based on an eye inspection of the photographs. Finally, we
visualise anomalous feature maps of steel using a strip and painted steel
inspection dataset
