  The dominant approach for surface defect detection is the use of hand-crafted
feature-based methods. However, this falls short when conditions vary that
affect extracted images. So, in this paper, we sought to determine how well
several state-of-the-art Convolutional Neural Networks perform in the task of
surface defect detection. Moreover, we propose two methods: CNN-Fusion, that
fuses the prediction of all the networks into a final one, and Auto-Classifier,
which is a novel proposal that improves a Convolutional Neural Network by
modifying its classification component using AutoML. We carried out experiments
to evaluate the proposed methods in the task of surface defect detection using
different datasets from DAGM2007. We show that the use of Convolutional Neural
Networks achieves better results than traditional methods, and also, that
Auto-Classifier out-performs all other methods, by achieving 100% accuracy and
100% AUC results throughout all the datasets.
