PCBDet: An efficient deep neural network object detection architecture for automatic PCB component detection on the edge
Bibliographic record
Abstract
Abstract There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time‐consuming and prone to error, especially at scale. There has thus been significant interest in automatic PCB component detection, particularly leveraging deep learning. While deep neural networks are able to perform such detection with greater accuracy, these networks typically require high computational resources, limiting their feasibility in real‐world use cases, which often involve high‐volume and high‐throughput detection with constrained edge computing resource availability. To bridge this gap between performance and resource requirements, PCBDet, an attention condenser network design that provides state‐of‐the‐art inference throughput while achieving superior PCB component detection performance compared to other state‐of‐the‐art efficient architecture designs, is introduced. Experimental results show that PCBDet can achieve up to 2× inference speed‐up on an ARM Cortex A72 processor when compared to an EfficientNet‐based design while achieving ∼2–4% higher mAP on the FICS‐PCB benchmark dataset.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".