Intelligent Automated Feeding System for Lumber Quality Check in Industrialized Construction
Bibliographic record
Abstract
Quality control is a crucial function in off-site construction, especially for maintaining the integrity of lumber materials. Natural wood defects, such as twists and crooks, can compromise structural strength, leading to production delays and increased material waste. This paper presents an intelligent system for real-time defect detection and prediction in lumber. The system integrates the YOLOv8 model (for efficient wood defect detection) with cubic spline interpolation (for precise defect localization and decision-making). With a precision of $99.03 \%$ and a recall of $100 \%$, the proposed system significantly reduces rework, material waste, and production delays. By providing accurate and timely defect information, this system supports effective quality control and enhances the overall efficiency of the manufacturing process in off-site wood construction. The system’s ability to predict defects ensures the production of highquality wood components for modular construction, improving operational workflows and reducing waste.
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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 | 0.000 |
| 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 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".