Thermal Imaging for Rockfall Detection
Bibliographic record
Abstract
ABSTRACT: Under a NIOSH research grant, the Geotechnical Center of Excellence (GCE) evaluated the capability and limitations of thermal cameras to detect rockfall events and the conditions that can lead to rockfall. This two-year study tested four different thermal imaging cameras in controlled rockfall experiments and long-term surveillance tests in various mines and conditions. The cameras can detect rockfall events at less than their resolution as rockfall creates larger craters, impacts, and dust trails visible in the thermal IR. The surveillance test determined that the cameras work in both day and night conditions under a wide range of conditions. However, fog, heavy precipitation (rain, snow, hail), and dust limited visibility in the thermal IR. Four thermal infrared cameras were acquired for the project. The cameras were installed on a mobile mine monitoring platform. The project tested thermal imaging cameras’ effectiveness to detect and record rockfall events and rockfall hazards in surface mining operations as a method to protect mine workers from the risks of rockfalls. One of the primary findings of the project is that off-the-shelf cameras can be used to increase situational awareness at the mines in low-light conditions. This presentation will document camera selection, resolution, and results from the project. 1. INTRODUCTION The Thermal Imaging project for Rockfall Detection Mobile Monitoring Platform (MMP) has been deployed at seven different open-pit mines for over one year. Since April 7, 2021, the MMP has been deployed with all four thermal cameras installed and operational at seven Western US and Canada mine sites. The system was initially deployed at two different mine sites in Arizona starting in January 2021 with three cameras. The research project has tested thermal imaging cameras’ effectiveness in detecting and recording rockfall events and rockfall hazards in surface mining operations as a method to protect mine workers from the risks of rockfalls. One of the primary goals is to identify cost-effective, off-the-shelf systems that can be integrated with existing slope monitoring systems or installed separately as a part of routine mine observations.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".