Research on Real-Time Collision Detection System for Data-Driven Tower Crane Simulation Monitoring and Visual Optimization
Why this work is in the frame
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Bibliographic record
Abstract
In order to improve the safety and efficiency of tower crane operation, a data-driven real-time collision detection system is proposed in this paper, which combines simulation monitoring and vision optimization technology. The research team collected operational data in real time through sensors and cameras, and used convolutional neural networks (CNN) to extract and classify image features to quickly identify potential collision risks. The experimental results show that the maximum detection accuracy of the system can reach 96.87% under various working conditions, but the missed detection rate and false alarm rate are reduced by 21.43% and 87.65% respectively compared with the traditional method. Therefore, this system provides an innovative solution for the safety management of tower cranes and promotes further development in the field of intelligent buildings.
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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 | 0.000 |
| 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 | 0.000 |
| 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