Two-dimensional high-precision non-contact automatic measurement method based on image corner coordinates
Bibliographic record
Abstract
Aiming at some shortcomings of the existing non-contact two-dimensional high-precision measurement methods, this paper proposes a two-dimensional high-precision non-contact automatic measurement method based on the corner coordinates of the image. Firstly, this paper designs a set of simple image acquisition device and explains the advantages of the Canny operator used in the image contour detection algorithm. Subsequently, this paper proposes a dimension calibration algorithm based on image corner coordinates, which can convert the pixel size to the actual size, and achieves the function of the algorithm by hierarchical, multi-step processing of the image. Finally, in order to realize the intelligent positioning and selection of the standard size workpiece position, an automatic measurement and positioning system is designed, which can convert the actual size signal into the pulse time control signal. The experimental results show that the measurement method proposed in this paper has the advantages of fast measurement speed, high robustness, low cost and high degree of automation. When using a black-and-white checkerboard paper with an accuracy of 0.1 mm, the measurement accuracy can reach the micron level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".