Dual-stream transformer-based activity classification for off-site construction productivity analysis
Bibliographic record
Abstract
This paper proposes a computer vision-based framework for automated productivity analysis in modular construction. A dual-stream transformer model classifies module installation activities based on on-site video data. The framework involves three main steps: object detection, activity classification, and productivity analysis. A convolutional neural network (CNN)-based object detector identifies key resources — cranes, modules, and workers — while the Transformer model analyzes the spatiotemporal changes in their movements. Six detailed installation activities are classified with high accuracy, achieving F1-scores above 0.98 across all classes. In contrast to previous rule-based or static image-based approaches, the proposed model captures the continuous and dynamic nature of operations, including transitions between activities. Productivity is evaluated by aggregating activity durations, which subsequently helps identify process bottlenecks. Post hoc video analysis further reveals the causes of delays. The proposed method supports real-time and data-driven monitoring, offering practical insights for improving operational efficiency. This framework provides a basis for future applications such as productivity forecasting and cross-site benchmarking.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".