Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
{Norimichi Ukita Yuki Kondo}

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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| crack-segmentation-on-khanhha-s-dataset-4x | CSSR (SR→SS) | Average IOU: 0.518 IoU_max: 0.587 |
| crack-segmentation-on-khanhha-s-dataset-4x | CSSR (SS→SR) | Average IOU: 0.558 IoU_max: 0.558 |
| crack-segmentation-on-khanhha-s-dataset-4x-1 | CSSR (w/ PSPNet) | AHD95: 24.74 Average IOU: 0.539 HD95_min: 21.20 IoU_max: 0.557 |
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