Contaminant detection using a CZT photon counting detector with TDI image reconstruction
Bibliographic record
Abstract
Abstract Food x-ray inspection systems are designed to detect unwanted physical contaminants in packaged food to maintain a high level of food safety for consumers. Modern day x-ray inspection systems often utilize line scan sensors to detect these physical contaminants but are limited to single or dual energies. However, by using a photon counting detector (PCD), a new generation of food inspection systems capable of acquiring images at more than two energy bins could improve discrimination between low density contaminants. In this work, five type of contaminants were embedded in an acrylic phantom and imaged using a cadmium zinc telluride (CZT) PCD with a pixel pitch of 330 μm. A set of images were acquired while the phantom was stationary, and another set of images were acquired while the phantom was moving to mimic the movement of a conveyor belt. Image quality was assessed by evaluating the contrast-to-noise ratio (CNR) for each set of images. For imaging times larger than 25 ms, the results showed that the moving phantom data set yielded larger CNR values compared to a stationary phantom. While conventional x-ray inspections often utilize line scan sensors, we report that physical contaminant detection is possible with a CZT PCD x-ray imaging system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".