Vision-Based Recognition of Dirt Loading Cycles in Construction Sites
Bibliographic record
Abstract
Automated control of production lines in different segments of manufacturing has been advanced due to emergence of sensing devices and the repetitive character of the processes. The construction industry, however, still suffers from inefficiencies in real-time control of activities due to the manual practice of monitoring, and the fragmented and temporary nature of construction projects. Several sensing technologies including computer vision-based systems have been introduced to address this issue on construction sites. Earthmoving projects are one of the most suitable areas to employ vision-based techniques to extract productivity data because it is possible to select clear sightlines and earthmoving equipment are relatively easy to recognize. In addition to challenges of developing efficient object detection and tracking algorithms, activity recognition based on collected data from detection and tracking engines is yet to be tackled. In this paper, a logical framework is introduced that combines object recognition, tracking, and rational events to recognize dirt loading to a dump truck by a hydraulic excavators, and measure the working cycles and idle times of the earthmoving plants. This logical algorithm showed promising performance in which the variations were caused by deficiencies of recognition and tracking engines rather than the activity recognition algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".