Real-time Quantitative Visual Inspection using Extended Reality
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

 In this study, we propose a technique for quantitative visual inspection that can quantify structural damage using extended reality (XR). The XR headset can display and overlay graphical information on the physical space and process the data from the built-in camera and depth sensor. Also, the device permits accessing and analyzing image and video stream in real-time and utilizing 3D meshes of the environment and camera pose information. By leveraging these features for the XR headset, we build a workflow and graphic interface to capture the images, segment damage regions, and evaluate the physical size of damage. A deep learning-based interactive segmentation algorithm called f-BRS was deployed to precisely segment damage regions through the XR headset. A ray-casting algorithm is implemented to obtain 3D locations corresponding to the pixel locations of the damage region on the image. The size of the damage region is computed from the 3D locations of its boundary. The performance of the proposed method is demonstrated through a field experiment at an in-service bridge where spalling damage is present at its abutment. The experiment shows that the proposed method provides sub-centimeter accuracy for the size estimation.
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