Enhancing welfare assessment: Automated detection and imaging of dorsal and lateral views of swine carcasses for identification of welfare indicators
Bibliographic record
Abstract
High animal welfare standards are essential for sustainable pork production. Current on-farm welfare assessments present challenges including cost, time consumption, and biosecurity risks. This study presents the initial step towards an automated animal welfare assessment system for swine carcasses. We introduce a computer vision system for capturing dorsal and lateral views of pig carcasses. The proposed system consists of a tracking and image acquisition system, which captures comprehensive lateral and dorsal views of pig carcasses as they pass by a camera installed at a slaughterhouse. During evaluation, the system achieved an accuracy of 92.5 % (74 out of 80 detections in a video with 40 carcasses) with minimal false positives and undetected positions, operating in real-time at 41.65 frames per second (FPS) with high confidence. The Detection manager module led to a remarkable reduction in erroneous detections, dropping from 38.75 % to 3.75 %. In addition to tail position data, leveraging head position data as an auxiliary mechanism substantially reduced missed detections to 6.25 % and erroneous detections to 1.25 %. This underscores the critical role of auxiliary mechanisms in enhancing system performance. This system marks the initial phase in developing an automated welfare assessment tool, paving the way for real-time welfare monitoring of carcasses in pork production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".