Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
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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Crack SegmentationSegmentationSemantic SegmentationSuper-ResolutionSimilar Papers 제목 키워드 기반
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