Abstract
The defect prediction in the manufacturing of steel is a critical challenge because it affects the quality and safety of the products. For this reason, intelligent image fusion approach is introduced in this research to enhance accurate prediction of defect types and locations in steel materials. By utilizing U-Net architecture and pretrained ResNet18 encoder layers, our method performs fusion of data from several imaging modalities thus supporting precise localization as well as classification of defects. In our model’s learning curves as well as comparing predicted segmentation masks with ground truth images, extensive experimentation and visualization show that our model captures subtle defects very well. By so doing, it exhibits robust performance that mitigates risks associated with overfitting since it can accurately identify any flaw while still having the ability to accept unseen data from other sources. These results suggest that our approach can highly contribute to improving quality control and safety standards for steel production.
Published Version
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