Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training
Tesfaye Getachew Shiferaw,
Li Yao
Abstract:Accurately detecting defects while reconstructing a high-quality normal background in surface defect detection using unsupervised methods remains a significant challenge. This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal background reconstruction without noise. We propose an adaptive weighted structural similarity (AW-SSIM) loss for focused feature learning. AW-SSIM improves structural similarity (SSIM) los… Show more
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