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An Online Rail Track Fastener Classification System Based on YOLO Models

In order to save manpower on rail track inspection, computer vision-based methodologies are developed. We propose utilizing the YOLOv4-Tiny neural network to identify track defects in real time. There are ten defects covering fasteners, rail surfaces, and sleepers from the upward and six defects abo...

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Detalles Bibliográficos
Autores principales: Hsieh, Chen-Chiung, Hsu, Ti-Yun, Huang, Wei-Hsin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783312/
https://www.ncbi.nlm.nih.gov/pubmed/36560339
http://dx.doi.org/10.3390/s22249970