Low False Alarm Rate Model for Unsafe-Proximity Detection in Construction
Bibliographic record
Abstract
The research reported in this paper proposes and develops an unsafe-proximity detection model focused on decreasing false alarms. By considering three types of entity attributes [i.e., (1) position, (2) heading/moving direction, and (3) speed], more accurate unsafe-proximity identifications with reduced false alarms can be achieved. The proposed and developed model works via two modules, as follows: (1) state tracking module, and (2) safety rules module. The state tracking module collects construction entities’ states (position, heading, and speed) in real time. The collected states information is analyzed in the safety rules module for unsafe-proximity identifications. Five common situations on construction jobsites are extracted and studied for the development of the safety rules, as follows: (1) static equipment and moving worker, (2) moving equipment and moving worker, (3) moving equipment and static worker, (4) two pieces of moving equipment, and (5) moving equipment and static equipment. The unsafe area around equipment is divided into alert and warning areas which are quantified using forklift as sample equipment. The localization accuracy of the state tracking module and the functional effectiveness of the safety rules module are evaluated, through simulation and a field experiment. Twelve scenarios and 13 subscenarios were designed and incorporated, in the simulation and the field experiment, respectively. The extended Kalman filter combined with the nearest-neighbor method was used in the simulation and a global positioning system (GPS)-aided inertial navigation system sensor was used in the field experiment as the state tracking module. The results suggest that the magnitude of localization accuracy of the extended Kalman filter combined with the nearest-neighbor method and the adopted sensor both are less than 0.7 m. Such an accuracy level is acceptable for construction applications. Moreover, the developed safety rules have a strong capability in avoiding false alarms. In some scenarios the developed model can avoid one false alarm for each scan. The research reported in this paper also demonstrates the applicability and feasibility of implementing the model for real applications. The developed model has great promise to enhance construction safety and mobility by timely avoiding collisions, while reducing false alarms and interruptions to work.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".