Integrating Canny Filter and Convolutional Neural Networks for Quality Defect Detection in Injection Molding Process
Bibliographic record
Abstract
Detection of quality defects in injection molding manufacturing remains one of the most challenging tasks due to its heavy reliance on human visual inspection, which has inherent limitations.Computer vision, which addresses image-based problems, offers promising solutions in this area.This article explores the application of machine vision models to identify quality defects in products from the injection molding process.The methodology is divided into two main steps: first, the application of the Canny filter to extract edge characteristics; and second, the use of Convolutional Neural Networks (CNN) to classify parts as either good or defective.The results demonstrate that the combined method outperforms the use of CNN alone, achieving an accuracy of 99.57%, a precision of 99.44%, a recall of 100%, and an F1-score of 99.72% with the Canny filter, compared to an accuracy of 95.31%, a precision of 94.24%, a recall of 100%, and an F1-score of 97.03% without the Canny filter.These findings confirm that the integrated model can be implemented in online production systems to enhance the detection of defects in injection molding processes.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".