Real time contaminants detection in wood panel manufacturing process using YOLO algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent technological advancements have significantly increased the autonomy of daily tasks within industries. These technologies are currently enabling the accomplishment of tasks previously deemed impossible for humans, such as continuous surveillance and detection of contamination at any stage of the production chain. For instance, in wood panel manufacturing, where metal particles may inadvertently be introduced before pressing the panel, causing wear and failure of pressing and cutting tools. This paper presents a methodology utilizing the YOLO (You Only Look Once) algorithms, to detect contaminants in the wood panel manufacturing chain. A combination of seven image quality degradation methods is proposed to increase the difficulty of detecting the objects. Resulting in the development of a detection tool with enhanced generalization capabilities. The results were compared using degraded and regular training datasets. It is demonstrated that training on degraded images improve precision, recall, mAP50, and mAP50-95. YOLOv9 surpasses all examined models, achieving a F1 score of 99.8%. An experimental investigation is also conducted to showcase the effectiveness of the YOLOv9 trained with a degraded dataset present a good capability for real object detection with the highest F1 score of 98%. The results with real data exhibit a strong agreement with numerical results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it