Research on the lightweight detection method of rail internal damage based on improved YOLOv8
Abstract To address the challenges of high computational costs, large storage demands, and low detection accuracy in internal rail damage identification, we propose a lightweight detection model, GhostMicroNet-YOLOv8n, as an enhancement of YOLOv8n. This model offers efficient and reliable technical...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
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| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-025-00584-1 |
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