This research investigates deep learning-based approach for defect detection in the steel production using Severstal steel dataset. The developed system integrates DenseNet121 for classification and DeepLabV3 for segm...This research investigates deep learning-based approach for defect detection in the steel production using Severstal steel dataset. The developed system integrates DenseNet121 for classification and DeepLabV3 for segmentation. DenseNet121 achieved high accuracy with defect classification as it achieved 92.34% accuracy during testing. This model significantly outperformed benchmark models like VGG16 and ResNet50, which achieved 72.59% and 92.01% accuracy, respectively. Similarly, for segmentation, DeepLabV3 showed high performance in localizing and categorizing defects, achieving a Dice coefficient of 84.21% during training and 69.77% during validation. The dataset includes steels which have four different types of defects and the DeepLab model was particularly effective with detection of Defect 4, with a Dice coefficient of 87.69% in testing. The model performs suboptimally in segmentation of Defect 1, achieving an accuracy of 64.81%. The overall system’s integration of classification and segmentation, alongside thresholding techniques, resulted in improved precision (92.31%) and reduced false positives. Overall, the proposed deep learning system achieved superior defect detection accuracy and reliability compared to existing models in the literature.展开更多
文摘This research investigates deep learning-based approach for defect detection in the steel production using Severstal steel dataset. The developed system integrates DenseNet121 for classification and DeepLabV3 for segmentation. DenseNet121 achieved high accuracy with defect classification as it achieved 92.34% accuracy during testing. This model significantly outperformed benchmark models like VGG16 and ResNet50, which achieved 72.59% and 92.01% accuracy, respectively. Similarly, for segmentation, DeepLabV3 showed high performance in localizing and categorizing defects, achieving a Dice coefficient of 84.21% during training and 69.77% during validation. The dataset includes steels which have four different types of defects and the DeepLab model was particularly effective with detection of Defect 4, with a Dice coefficient of 87.69% in testing. The model performs suboptimally in segmentation of Defect 1, achieving an accuracy of 64.81%. The overall system’s integration of classification and segmentation, alongside thresholding techniques, resulted in improved precision (92.31%) and reduced false positives. Overall, the proposed deep learning system achieved superior defect detection accuracy and reliability compared to existing models in the literature.
