Learning monocular depth estimation for defect measurement from civil RGB-D dataset
Bibliographic record
Abstract
A large quantity of civil infrastructure in North America is near the end of their design life. Consequently, the routine visual structural inspection is increasingly necessary to ensure the safety and efficient management of the infrastructure stock. The increasing need for inspections and the laborious nature of the work has caused strain on the inspection industry. To improve inspection efficacy, various researchers have proposed novel deep learning methodologies to automatically classify, detect, and segment structural defects from images. After the defects are identified, it is often desirable to quantify the size of the defect, for severity classification and repair cost estimation. Yet, the measurement from a single image for quantification is not a trivial task, requiring supplementary data or sensor inputs, which may not be practical or economical in the current inspection process. In this study, we propose to recover the three-dimensional geometry of a scene from a single image, by using deep learning-based monocular depth estimation. The monocular depth estimation field has made great progress by leveraging deep learning and a plethora of open red, green, blue, and depth (RGB-D) datasets. However, there has not been a publicly available in situ Light Detection and Ranging (LiDAR) RGB-D dataset for the civil engineering domain, which is a barrier for researchers to develop and evaluate spatial computer vision methods in the civil engineering context. To bridge this gap, we build a LiDAR-based RGB-D dataset for training monocular depth estimators. Then using the civil RGB-D dataset, we test a solution for the real-world application of monocular depth estimation to quantify defects in civil infrastructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".