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Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution

2021-07-25International Conference on Machine Vision and Applications (MVA)Code Available1· sign in to hype

Yuki Kondo, Norimichi Ukita

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Abstract

This paper proposes a method for crack segmentation on low-resolution images. Detailed cracks on their high-resolution images are estimated by super resolution from the low-resolution images. Our proposed method optimizes super-resolution images for the crack segmentation. For this method, we propose the Boundary Combo loss to express the local details of the crack. Experimental results demonstrate that our method outperforms the combinations of other previous approaches.

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