  Surface inspection systems are an important application domain for computer
vision, as they are used for defect detection and classification in the
manufacturing industry. Existing systems use hand-crafted features which
require extensive domain knowledge to create. Even though Convolutional neural
networks (CNNs) have proven successful in many large-scale challenges,
industrial inspection systems have yet barely realized their potential due to
two significant challenges: real-time processing speed requirements and
specialized narrow domain-specific datasets which are sometimes limited in
size. In this paper, we propose CNN models that are specifically designed to
handle capacity and real-time speed requirements of surface inspection systems.
To train and evaluate our network models, we created a surface image dataset
containing more than 22000 labeled images with many types of surface materials
and achieved 98.0% accuracy in binary defect classification. To solve the class
imbalance problem in our datasets, we introduce neural data augmentation
methods which are also applicable to similar domains that suffer from the same
problem. Our results show that deep learning based methods are feasible to be
used in surface inspection systems and outperform traditional methods in
accuracy and inference time by considerable margins.
