Towards Part-Based Construction Equipment Pose Estimation Using Synthetic Images
Why this work is in the frame
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Bibliographic record
Abstract
Monitoring the pose of the construction equipment is an essential prerequisite for determining safety and productivity of construction processes. In order to make construction sites safer, there is high demand for accurate and real-time motion tracking tools and methods to capture any movement of the equipment and its parts. Computer vision (CV) techniques are becoming more popular because of their lower cost of deployment and availability compared with other techniques. However, few CV methods have been focused on equipment part detection and pose estimation. This paper aims to propose a new method to detect the parts of the construction equipment that can be used to detect its pose. Using the concept of synthetic images for each equipment’s part (e.g., bucket, dipper, boom, and body), multiple detectors are trained for each part from different views and applied to recognize the parts. The synthetic images are generated by overlaying the images of the 3D model of the equipment on the real images of the construction sites as background.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it