Classification of Surface Defects on Steel Sheet Images Using DenseNet121 Architecture

Classifying surface defects is vital for steel sheet manufacturers. The conventional approaches have obtained moderate accuracies in terms of classifiers, while these methods have developed by depending on experts or different projects. DenseNet121 model, a machine-vision-based classification approach was proposed to overcome the drawbacks of traditional approaches. The goal of this paper is to apply pre-trained DenseNet121 network for classifying the steel defects categorized as rolled-in scales, patches, crazing, pitted surface, inclusion, and scratches. Fine-tuning transfer learning and k-fold cross-validation were implemented to train and evaluate the performance of the model. Additionally, this study uses Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD) algorithms to optimize the model parameters. The testing result showed that all 5 folds were over 98.5% accuracy for both Adam and SGD optimizers. It also found that a gradient-weighted class activation mapping (Grad-CAM) was a good technique to visualize the surface failure locations of steel sheets. The findings indicated the ability of the proposed method to automatically classify the steel surface defect statuses.