Automatic Detection of Railway Faults Using Neural Networks: A Comparative Study of Transfer Learning Models and YOLOv11

Automatic Detection of Railway Faults Using Neural Networks: A Comparative Study of Transfer Learning Models and YOLOv11

Developing reliable railway fault detection systems is crucial for ensuring both safety and operational efficiency. Various artificial intelligence frameworks, especially deep learning models, have shown significant potential in enhancing fault detection within railway infrastructure. This study exp...

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Bibliographic Details
Main Authors: Omar Rodríguez-Abreo, Mario A. Quiroz-Juárez, Idalberto Macías-Socarras, Juvenal Rodríguez-Reséndiz, Juan M. Camacho-Pérez, Gabriel Carcedo-Rodríguez, Enrique Camacho-Pérez
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Infrastructures
Subjects:
deep learning
transfer learning
YOLOv11
railway fault detection
safety
maintenance
Online Access:https://www.mdpi.com/2412-3811/10/1/3
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