BoltResvit: an enhanced residual vision transformer for robotic-assisted nondestructive railway bolt looseness monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Railway bolt looseness threatens structural integrity and operational safety, necessitating robust automated monitoring methods. However, limited availability and severe class imbalance of bolt image data from practical railway environments greatly restrict the training effectiveness and generalisation capability of deep learning models. This paper proposes a robotic-assisted non-destructive monitoring approach termed BoltResViT, integrating generative adversarial network (GAN)-based data augmentation and a residual vision Transformer (ResNet-Transformer) model. StyleGAN2-ADA generates high-fidelity synthetic samples validated by Fréchet inception distance (FID) and expert assessment, constructing a balanced training dataset. The ResNet-Transformer model integrates local spatial feature extraction capabilities of ResNet18 with the global contextual modelling ability provided by Transformer-based multi-head self-attention. Furthermore, a channel attention module and auxiliary monitoring branch are incorporated to enhance feature discrimination and model robustness. A dual-supervision mechanism combining primary and auxiliary monitoring branches ensures accurate bolt looseness monitoring. Experimental results demonstrate that the proposed BoltResViT achieves monitoring accuracy of 99.28%, showing excellent generalisation and practical applicability in railway bolt monitoring scenarios characterised by limited data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it