Super-resolution knowledge-distillation-based low-resolution steel defect images classification

Abstract

Due to cost and equipment limitations, steel surface defect images are often of low resolution, significantly impairing the recognizability of key features within the images and thereby reducing the accuracy of automated detection. To address this issue, this paper proposes a classification method for low-resolution steel defect images based on super-resolution knowledge distillation. This method initially employs advanced super-resolution techniques to reconstruct low-resolution images, aiming to restore critical image details and features. Subsequently, knowledge distilled from deep learning models trained on high-resolution images is transferred to models specifically designed for low-resolution images. This approach combines the advantages of super-resolution image reconstruction and the efficiency of knowledge distillation. It not only enhances the quality of low-resolution images but also maintains the lightweight nature of the model, making it suitable for real-time detection scenarios. A series of experiments conducted on publicly available steel defect dataset demonstrate that the method proposed effectively enhances the classification accuracy of low-resolution steel defect images. This achievement is of significant importance in advancing industrial automated defect detection technologies and provides a new research direction in the field of low-resolution image processing.