Unsupervised saliency detection of rail surface defects using stereoscopic images
Visual information is increasingly recognized as a useful method to detect rail surface defects due to its high efficiency and stability. However, it cannot sufficiently detect a complete defect in the complex background information. The addition of surface profiles can effectively improve this by i...
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| Main Authors: | , , , , , |
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| Format: | Default Article |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/2134/13337936.v1 |
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