AI-Powered Smart Defect Detection System for Printed Circuit Boards
Bibliographic record
Abstract
In electronics manufacturing companies PCB defect detection is needed to see the quality of products whether it is good. basically in which this process is done by people and machine in which this takes so much time and also it is expensive. So overcome this problem ,we have implemented Smart Defect Detection System that can detect the PCB issue by using AI. In which we have used two models YOLOv11, MobileNet. YOLOv11 model is used for detecting the surface area problem like missing components, misalignment parts, solder bridges. MobileNet Model is basically used for checking the internal circuit patterns, in which small and hidden defects can be detected. These two models are combined with OpenCV and python to make a low cost and efficient inspection tool. In which it includes Graphical User Interface(GUI) so that non technical user can also able to upload the PCB images and can see the defected issues in the screen. This helps mainly for small electronic manufacturing companies where we can reduce their cost. In increases accuracy, reducing human errors and in which it makes the inspection process faster and reliable. the experiment result shows it is a good balance between performance, cost, flexibility, making it suitable for real time application.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".