Steel Surface Defect Detection Using an Ensemble of Deep Residual Neural Networks

Steel defect diagnostics is important for industry task as it is tied to the product quality and production efficiency. The aim of this paper is evaluating the application of residual neural networks for recognition of industrial steel defects of three classes. Developed and investigated models based on deep residual neural networks for the recognition and classification of surface defects of rolled steel. Investigated the influence of various loss functions, optimizers and hyperparameters on the obtained result and selected optimal model parameters. Based on an ensemble of two deep residual neural networks ResNet50 and ResNet152, a classifier was constructed to detect defects of three classes on flat metal surfaces. The proposed technique allows classifying images with high accuracy. The average binary accuracy of classifying the test data is 96.7% for all images (including defect-free ones). The fields of neuron activation in the convolutional layers of the model were investigated. Feature maps formed in the process were found to reflect the position, size, and shape of the objects of interest very well. The proposed ensemble model has proven to be robust and able to accurately recognize steel surface defects. Erroneous recognition cases of the classifier application are investigated. It was shown that errors most often occur in ambiguous situations, where surface artifacts of different types are similar.

Automated summary

Steel defect diagnostics is crucial for industry task, and this paper evaluates the application of residual neural networks for recognizing industrial steel defects. By exploring different model parameters and loss functions, the optimal model parameters were selected, and a classifier was constructed to accurately detect three classes of steel defects. The investigation of neuron activation in the model's convolutional layers revealed that the feature maps well reflected the position, size, and shape of the defects. The proposed ensemble model proved to be robust and capable of accurately recognizing steel surface defects.