3D modelling of industrial piping systems using digital photogrammetry and laser scanning
Bibliographic record
Abstract
Industrial metrology is one of the fastest growing areas in advanced technologies, such as electronics and optics, computation speed, and it has been increased in recent years. There are several tools used in industrial metrology, such as total stations, digital photogrammetry, and laser scanning. Close range digital photogrammetry has been implemented for an industrial piping system in terms of installation, inspection, and replacement. Laser scanning is also used for industrial measurements to generate 3D coordinates points. In order to develop a 3D modelling strategy, this thesis focuses on the development, selection and design of photogrammetric procedures and project specific targets. This thesis also explores image-acquiring sensors such as digital cameras and laser scanners in terms of their capabilities and advantages. Based on experimental setup accuracy, measurements of piping systems are compared for applications of two different sensors. Effects of different surface materials are examined in laser scanning applications and several different types of materials are used for acquiring point clouds data. Measurement of pipes' diameters and residual analyses are conducted with different surface materials, which are used for industrial pipes. Significant improvement in laser scanning data acquisition is examined in terms of data quality both quantitatively and qualitatively during the residual analyses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".