Liangfu Li, Min Hu. Method for Small-Bridge-Crack Segmentation Based on Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101004
- Laser & Optoelectronics Progress
- Vol. 56, Issue 10, 101004 (2019)
Fig. 1. Schematic of low-resolution subgraph segmentation. (a) Original image; (b) schematic of segmentation; (c) low-resolution subgraphs after segmentation
Fig. 2. Structural diagram of discriminator
Fig. 3. Structural diagram of Discrimination branch
Fig. 4. Structural diagram of segmentation branch
Fig. 5. Comparison chart of generated network structures
Fig. 6. Bridge-crack image and manually labelled semantic segmentation image. (a) Bridge-crack image; (b) manually labelled segmentation image
Fig. 7. Comparison of segmentation results using different algorithms. (a) Original images; (b) labelled images; (c) AdaptNet; (d) FC-DenseNet; (e) PSPNet; (f) SE-GAN
Fig. 8. Comparison of small-bridge-crack segmentation results. (a) Original images; (b) labelled images; (c) AdaptNet; (d) FC-DenseNet; (e) PSPNet; (f) SE-GAN
Fig. 9. Effect of loss on PSNR of generated image
Fig. 10. Comparison of effect of loss function. (a) Original images; (b) low-resolution images; (c) Ladv; (d) Ladv+Lp; (e) Ladv+Lp+Lseg
Fig. 11. Super-resolution images generated by different discriminators. (a) Original images; (b) low-resolution images; (c) traditional discriminator; (d) discrimination branch
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Table 1. Comparison of segmentation results using different algorithms%
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Table 2. Effect of loss function on generated image
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Table 3. Quality of super-resolution images generated by different discriminators
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