Optimal Spatial Resolution of Omnidirectional Imaging Systems for Pipe Inspection Applications
Bibliographic record
Abstract
Achieving optimal spatial resolution in imaging systems plays a major role in the design of vision-based industrial inspection tools. Single-view omnidirectional imaging systems provide a cost-effective and computationally-traceable solution for real-time inspection of infrastructure with a favorable size factor. We formulate, for the first time, the spatial cylindrical resolution of omnidirectional Catadioptric and Dioptric imaging systems with the focus on pipe inspection applications. We also provide a design guideline to achieve the highest resolution in these systems. First, we deliver a comprehensive study on optimal resolution in Catadioptric imaging systems which consist of a perspective pinhole camera, a collimated laser as the light source, and a reflective surface (i.e., hyperbolic mirror). Variation of the spatial resolution in terms of the camera's focal length, the mirror curvature, and the relative position between the laser projector and the camera is fully investigated via simulation and experiments. Also, the optimal resolution in Dioptric systems, which consist of a camera with compound refractive lenses (i.e., fish-eye lens) is studied and compared with that in Catadioptric systems. Tests were conducted on a 40-cm-diameter PVC pipe in a controlled laboratory environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".