Ruhai Zhao, Fangbin Wang. Polarization Thermal Image Segmentation Algorithm of Metal Fatigue Based on Gray Level and Information Entropy Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2410012
- Laser & Optoelectronics Progress
- Vol. 58, Issue 24, 2410012 (2021)
Fig. 1. Infrared polarization information images. (a) 0° azimuth image; (b) 60° azimuth image; (c) 120° azimuth image; (d) s0 image; (e) s1 image; (f) s2 image; (g) DOP image; (h) AOP image
Fig. 2. Process of searching potential target regions. (a) Fusion results of multi azimuth image gray level and information entropy; (b) potential target region images
Fig. 3. Simulation segmentation results of 0°, 60°, 120° azimuth images
Fig. 4. Flow chart of multi-channel infrared polarized thermal image overall segmentation
Fig. 5. Specimen size
Fig. 6. Original images
Fig. 7. Process of multi-channel segmentation with fatigue cycle of 1000 times
Fig. 8. Process of multi-channel segmentation with fatigue cycle of 8000 times
Fig. 9. Process of multi-channel segmentation with fatigue cycle of 14000 times
Fig. 10. Comparison of segmentation results of different algorithms
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Table 1. Some parameters and calculation results of potential target region at fatigue cycle of 1000 times
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Table 2. Some parameters and calculation results of potential target region at fatigue cycle of 8000 times
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Table 3. Some parameters and calculation results of potential target region at fatigue cycle 14000
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Table 4. GLC of different algorithms unit: %
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Table 5. δR of different algorithms unit: %
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