  Automatic defect recognition is one of the research hotspots in steel
production, but most of the current methods mainly extract features manually
and use machine learning classifiers to recognize defects, which cannot tackle
the situation, where there are few data available to train and confine to a
certain scene. Therefore, in this paper, a new approach is proposed which
consists of part of pretrained VGG16 as a feature extractor and a new CNN
neural network as a classifier to recognize the defect of steel strip surface
based on the feature maps created by the feature extractor. Our method achieves
an accuracy of 99.1% and 96.0% while the dataset contains 150 images each class
and 10 images each class respectively, which is much better than previous
methods.
