| Title: |
Toward smart railway maintenance: AI-enhanced Non-Destructive Evaluation using Vision Transformers and CNNs for fastener defect detection |
| Authors: |
Samira Mohammadi; Sasan Sattarpanah Karganroudi; Mehdi Adda; Hussein Ibrahim |
| Source: |
Green Energy and Intelligent Transportation, Vol 5, Iss 3, Pp 100332- (2026) |
| Publisher Information: |
Elsevier |
| Publication Year: |
2026 |
| Collection: |
Directory of Open Access Journals: DOAJ Articles |
| Subject Terms: |
Railway fastener defect detection; Machine learning; CNN; Transformers; Transportation engineering; TA1001-1280; Renewable energy sources; TJ807-830 |
| Description: |
Predictive health management and maintenance of transport infrastructure are critical for preventing accidents and service disruptions. Applying Non-Destructive Evaluation (NDE) and imaging techniques is essential for identifying irregularities without causing harm. This research utilizes pre-trained models and incorporates transfer learning concepts to overcome dataset constraints. This study assesses the effectiveness of various machine learning models, including the Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), VGG19, VGG16, and ResNet50, in enhancing NDE for railway track fasteners. ViT and DeiT, both transformer-based models, emerged as the top performers due to their superior learning efficiencies and generalization capabilities, augmented by precise hyperparameter tuning. VGG models are a reliable alternative, while ResNet50 is better suited for applications prioritizing computational efficiency over accuracy. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2773153725000829; https://doaj.org/toc/2773-1537; https://doaj.org/article/d16179eb5084419e98e03fb8ea8ce743 |
| DOI: |
10.1016/j.geits.2025.100332 |
| Availability: |
https://doi.org/10.1016/j.geits.2025.100332; https://doaj.org/article/d16179eb5084419e98e03fb8ea8ce743 |
| Accession Number: |
edsbas.60D81799 |
| Database: |
BASE |