Automated steel surface defect detection and classification using a new deep learning-based approach

Abstract

In this study, a new deep learning-based approach has been developed that detects and classifies surface defects that occur in the steel production process. The proposed methodology was created in four steps. In the first step, a deep learning model is designed that trains the residual and attention structures in parallel, thus increasing the classification performance. In the second step, deep features were extracted from the Parallel Attention Residual-Convolutional Neural Network model. The extracted features in the third step were selected by a new and simple algorithm (NCA-ReliefF Matched Index) based on matching the indexes obtained from the Neighborhood Component Analysis and Relief algorithms. In the last process, classification was done with the support vector machine algorithm. The proposed methodology was used for dual and multi-class classification tasks and evaluated on a dataset in the Kaggle database named Severstal: Steel Defect Detection.