Steel Surface Defect Classification Using an Improved VGG16-Based Model

Document Type : Original Article

Authors

1 PhD Candidate, Department of Computer Engineering, Islamic Azad University, Yazd, Iran

2 Assistant Professor, Department of Computer Engineering, Islamic Azad University, Yazd, Iran

10.22034/abmir.2025.23563.1162

Abstract

Steel sheets are among the key components used in the automotive industry. Accurate classification of surface defects in these sheets plays a vital role in ensuring product quality. The main challenge in this field lies in the inspection process, as traditional manual inspection methods suffer from significant limitations caused by human error. Although deep learning algorithms have recently emerged as effective and automated approaches for defect detection, the classification of small and complex defects remains challenging and requires optimized modeling of spatial and channel information.In this study, a novel model based on the VGG16 architecture and transfer learning is proposed. The model integrates a two-dimensional spatial attention module and an attention-based magnification mechanism to suppress background noise and focus on the key regions of the image. After evaluation on two benchmark datasets, NEU-CLS and NEU-DET, the proposed model achieved classification accuracies of 99.98% and 99.92%, respectively—showing an improvement of at least 4% over the baseline VGG16 model and approximately 0.018% compared with previous studies. These results demonstrate the superior performance of the proposed approach in accurately classifying steel surface defects. Finally, for practical deployment, a web application was developed and made available to industry experts.

Keywords

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References

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Applied and basic Machine intelligence research
Volume 3, Issue 2
December 2025
Pages 73-88

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