Error-Correction Methods for Construction Site Image Processing under Changing Illumination Conditions
Bibliographic record
Abstract
Image processing is an effective tool for automated monitoring of construction projects. For the last decade, it has gained increasing acceptance in its application to progress monitoring, productivity analyses, and quality assurance. However, a notable downside exists in image processing, especially in outdoor applications such as construction project monitoring: image quality is heavily affected by ambient lighting conditions. Poor or undesirable lighting conditions produce substandard quality images, which generally lead to a high level of errors in the related image processing for information extraction. This paper presents error-correction methods that can improve the image processing results for construction progress monitoring in the postprocessing stage. The methods are applied in the postprocessing stage. The key idea behind the error-correction methods is the concept of priority to classify input images and project information into several categories based on data reliability and intelligently use the classified information for more accurate analyses of the project progress. Tests in real construction sites showed that these postprocessing methods significantly increased the accuracy of image processing-based construction progress monitoring.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".