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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hsieh, Chen-Chiung, Hsu, Ti-Yun, Huang, Wei-Hsin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783312/
https://www.ncbi.nlm.nih.gov/pubmed/36560339
http://dx.doi.org/10.3390/s22249970

Ejemplares similares