Deep learning-based surface defect detection in steel products using convolutional neural networks

HIGHLIGHTS

• What: This study evaluated several Convolutional Neural Network (CNN) classifiers: ResNet-50 and to determine the most effective model for defect detection. is distinguished by its scalable architecture that adapts efficiently across various image dimensions making it ideal for high-accuracy applications on limited computational resources. In this paper, a comparative analysis was conducted among three CNN-based classifiers, namely EfficientNetB3, ResNet50, and MobileNetV2, to construct an enhanced classifier for proposed algorithms on other real dataset for predicting the remaining lifetime of the components based on the classification of its defect severity stages. end steel surface defect . . .
  

   

  

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