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3 months ago

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

{Norimichi Ukita Yuki Kondo}

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

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.

Benchmarks

BenchmarkMethodologyMetrics
crack-segmentation-on-khanhha-s-dataset-4xCSSR (SR→SS)
Average IOU: 0.518
IoU_max: 0.587
crack-segmentation-on-khanhha-s-dataset-4xCSSR (SS→SR)
Average IOU: 0.558
IoU_max: 0.558
crack-segmentation-on-khanhha-s-dataset-4x-1CSSR (w/ PSPNet)
AHD95: 24.74
Average IOU: 0.539
HD95_min: 21.20
IoU_max: 0.557

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