Hardware-Software Complex for Laser Scanning in Color Sorting Machines
Bibliographic record
Abstract
The paper concerns the problem of grain mixture analysis based on processing images synthesized during line-by-line scanning of each object in color sorting systems. The paper presents a hardware-software complex for sorting objects in the real time. The hardware part of the complex consists of two blocks: light source and device for reception and processing of images. The light source is a laser, passed through optical fiber and linearly expanded across the entire width of the photoseparator’s chute. Linear laser scan produces significant intensity of illumination. It is sufficient for working on the transmission of radiation through objects. The software part of the complex also consists of two blocks: the thresholding algorithm and an automated program for finding the optimal parameters of sorting on the basis of that algorithm. The algorithm calculates the number of connected defective pixels with arbitrary shape. Automated program works on the basis of pre-formed images of the objects of two classes: good and defective. As a result, the program displays in tabular form the most optimal sorting parameters. The program shows the dependence of the loss of good product from the missouts of the defective objects. The customer gets a clear choice of the most suitable sorting results. This complex was tested for sorting of unshelled rice seeds via transmission. It is shown that the complex allows to effectively detect hidden seed defects: red pigmentation, immaturity, fungal diseases, and others.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".