Automatic Identification of Idling Reasons in Excavation Operations Based on Excavator–Truck Relationships
Why this work is in the frame
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Bibliographic record
Abstract
Excavators and trucks are important equipment for earthwork operations, which make major contributions to construction productivity. To control the work efficiency and productivity of earthwork equipment, computer vision (CV) methods have been proposed to monitor equipment operations from site surveillance videos. Existing methods can recognize equipment activities to estimate the working and idling times. Idling time is an important factor that influences equipment productivity; however, the causes of equipment idling have not been considered in previous CV methods. Therefore, this research proposes a method to identify the main causes of excavator and truck idling by analyzing their interactive operations. First, the activities of the excavators and trucks are identified using convolutional neural networks. Then, work groups of excavators and trucks are clustered. Finally, the relationships between each excavator and the surrounding trucks are analyzed to identify the potential reason for idling. The proposed method was validated with videos from several construction sites, and the results were promising.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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