An enhanced network for extracting tunnel lining defects using transformer encoder and aggregate decoder
Bibliographic record
Abstract
The tunnel environment is characterized by insufficient ambient light, obstructed view, and complex inner lining construction conditions. These factors frequently result in limited anti-interference capability, reduced recognition accuracy, and suboptimal segmentation results for defect extraction. We propose a deep network model utilizing an encoder–decoder framework that integrates Transformer and convolution for comprehensive defect extraction. The proposed model utilizes an encoder that integrates a hierarchical Transformer backbone with an efficient attention mechanism to fully explore complete information at multi-scale granularities. In the decoder, multi-scale information is initially aggregated using a Multi-Layer Perceptron (MLP) module. Additionally, the Stacking Filters with Atrous Convolutions (SFAC) module are implemented to enhance the perception of the complete defect scope. Furthermore, a Boundary-aware Attention Module (BAM) is implemented to enhance edge information to improve the detection of defects. With this well-designed decoder, the multi-scale information from the encoder can be fully aggregated and exploited for complete defect detection. Experimental findings illustrate the effectiveness of our proposed approach in addressing tunnel lining defects within the image dataset. The outcomes reveal that our proposed network achieves an accuracy (Acc) of 94.4% and a mean intersection over union (mIoU) of 78.14%. Compared to state-of-the-art segmentation networks, our model improves the accuracy of tunnel lining defect extraction, showcasing enhanced extraction effectiveness and anti-interference capability, thus meeting the engineering requirements for defect detection in complex environments of tunnels. • A novel enhanced deep learning architecture combining Transformer with convolution is proposed. • An advanced Transformer backbone capable of exploring both local fine-grained and global contextual information. • A well-designed decoder enhances the perceptual range to fully extract defect regions with multiple granularities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".