Digital Imaging in Assessment of Construction Project Progress
Why this work is in the frame
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Bibliographic record
Abstract
Digital Imaging in Assessment of Construction Project Progress Y. Wu, H. Kim Abstract: This paper presents a research effort designed to produce a digital imaging-based method to efficiently assess the level of construction project progress. Cameras are widely used to monitor and record various activities on a construction site. The images produced by the cameras can be processed with digital imaging techniques to help project participants better understand the status of the project. As the first step of this on-going research, this paper focuses on an image segmentation method designed to distinguish objects of interest, such as structural members on a construction site, from other objects in the image. The segmentation method combines an edge-based segmentation method with human knowledge of the construction scene represented by image morphological operations. A promising initial research result is also presented. Keywords: Canny Edge, Digital Imaging, Morphological Transformations, Project Control DOI: https://doi.org/10.22260/ISARC2004/0093 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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