Real-Time Leak Localization in N95 Respirators Using Infrared Imaging and Deep Learning with Optimal ROI Signal Correlation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A secure seal in N95 respirators is critical for preventing airborne contaminants, yet minor leaks significantly compromise protective efficiency. This paper presents an integrated framework leveraging infrared imaging and deep learning for real-time, non-contact leak localization. A custom U-Net model extracts the mask region from thermal images, while the Segment Anything Model 2 (SAM2) dynamically tracks contour variations under changing conditions. Thermal signals along the mask boundary are transformed into the frequency domain and correlated with a reference breathing signal to identify leak locations. The proposed method systematically evaluates optimal central region selection to enhance correlation-based leak detection. Experimental validation using pixel-wise Breathing Cycle Optical Flow Tracking demonstrates the effectiveness of this approach in accurately detecting and localizing leaks in real-world conditions, offering a robust alternative to conventional contact-based fit-testing methods.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it