Multi-input dual-branch reverse distillation for screw surface defect detection

Defect detection holds significant importance in industrial manufacturing as it aims to improve production quality and efficiency by identifying surface defects in products. Although previous works have shown excellent performance on many common benchmark datasets, they are often limited in terms of generality and their ability to detect logical anomalies. In this paper, we propose a dual-branch reverse distillation framework specifically designed for screw detection. By extracting global information through the global branch and combining it with local information, this framework enables the simultaneous detection of both logical and structural anomalies. To recover the information lost in compressed feature representation and improve the reconstruction ability of the decoder, we introduce a novel decoder structure that integrates multiple additional image input modules into the original network, allowing for various combinations of composite information with the input image during the image reconstruction process. Experimental results demonstrate the effectiveness of our proposed method on benchmark datasets.