FHENet: lightweight feature hierarchical exploration network for real-time rail surface defect inspection in RGB-D images
In recent years, computer vision systems have been increasingly applied to rail defect inspection. Rail defects should be identified quickly and accurately to ensure safe, stable, and fast train operations and thereby reduce the incidence of accidents and economic losses. As most existing methods fo...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/170750 |
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| Institution: | Nanyang Technological University |
| Language: | English |