Recognition of Electronic Component Orientations from Hand-Drawn Circuit Schematics through a Two Stage Machine Learning System
Bibliographic record
Abstract
Large strides in Artificial Intelligence (AI) and Machine Learning (ML) have lead to the automation of countless manual and time intensive tasks in circuit analysis and simulation. As hand-drawing circuits is often a critical time-consuming step, automating the netlisting and schematic-generation will greatly speedup this manual process. With emerging traction towards AI, research is increasingly being conducted in this field to develop component detection and classifier models. In this paper, a novel two stage detection system with a focus on component accuracy and orientations is developed. Using a hand-drawn circuit dataset of 2304 images by various authors with different drawing styles, an object-detection model was developed using Faster-RCNN, and YOLOv5 to recognize circuit components. Through a custom created dataset of 84 orientation classes for 15 types of electronic components, a secondary classification model was developed using ResNet-50 for the recognition of orientation information with an accuracy of 99.97%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".