YOLOv11-Fiducial: Real Time Detection of Fiducial Marker Points on Printed Circuit Boards for Electronic Manufacturing Services
Bibliographic record
Abstract
In electronic hardware design, fiducial markers are intentionally placed on Printed Circuit Boards (PCBs) to serve as reference points for alignment during various automated manufacturing processes. These markers serve as key element in ensuring accurate positioning are essential for orientation for PCBs throughout Surface Mount Device (SMD) placement, Through-Hole (TH) component insertion, soldering, assembly, and inspection procedures. Since PCBs can shift or skew along the cartesian plane as they move through production lines—which cannot be precisely adjusted to the exact width of each board—fiducial markers are reliable for realigning them at each stage of manufacturing. The motivation behind this study is to accurately and efficiently detect fiducial points on PCBs using the object detection model. To achieve this, a custom object detection model named YOLOv11-Fiducial was developed. The model achieved an F1 score of 0.9911 and Intersection over Union (IoU) based mAP50-95 of 89.45 to ensure high detection performance and reliability. To train the YOLOv11-Fiducial model, a comprehensive dataset named Fiducial-Dataset was created, covering a wide variety of PCB types, colors, and fiducial marker point styles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".