Qi Zhang, Bo Ye, Siqi Luo, Honggui Cao. Aluminum Plate Defect Image Segmentation Using Improved Generative Adversarial Networks for Eddy Current Detection[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0815002
- Laser & Optoelectronics Progress
- Vol. 58, Issue 8, 0815002 (2021)
Fig. 1. Generator network model
Fig. 2. Architecture of attention mechanism
Fig. 3. Discriminator network model
Fig. 4. Aluminum plate defect image segmentation using improved generative adversarial networks for eddy current detection
Fig. 5. Eddy current testing experiment platform
Fig. 6. Schematic of test-piece dimension
Fig. 7. Eddy current inspection images of aluminum plate defect
Fig. 8. Segmentation results of different methods. (a) Original image; (b) truth image; (c) Otsu method; (d) FCN-8s model; (e) FCN-32s model; (f) U-Net model; (g) proposed method
Fig. 9. Segmentation results of eddy current testing images under different signal-to-noise ratios. (a) Original image; (b) truth image; (c) Otsu method; (d) FCN-8s model; (e) FCN-32s model; (f) U-Net model; (g) proposed method
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Table 1. Comparison of segmentation results using different methods
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Table 2. Comparison of segmentation methods for eddy current testing image under different signal-to-noise ratios
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