Stereo Vision Based Worker Detection System for Excavator
Bibliographic record
Abstract
Stereo Vision Based Worker Detection System for Excavator H. Ishimoto, T. Tsubouchi Pages 1004-1012 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: It always has been a serious issue to improve safety in mine sites, demolition work sites, and civil engineering work sites. Especially, collision accidents caused by construction equipment damage not only the productivity of operations but also the health and life of workers in those work sites. To solve this problem, a variety of worker auto-detection means are developed so far, but they have not still become widely used in actual worksites. There have been two controversial points in the prior methods. One is that they were not enough in respect of the detection accuracy and certainty, and the other one is that the information of the detection results was not comprehensible to the operator of equipment. In this paper, the functional requirements of the obstacle detection system in earth moving work site is redefined at first, and then, it is proposed to apply a stereo vision to the obstacle detection system for an excavator. The system consists of horizontally arrayed two digital cameras with low distortion lens, an image processing controller, and a monitor for an operator in the cab. Stereo vision calculates the depth image from images of two cameras and detects the presence of obstacles, its distance, direction, and dimensions. Sensed obstacles are informed to the operator in the monitor image. Preliminary experiments are conducted with a stereo vision system mounted on a real equipment in a demonstration site. The result shows that two controversial points of the conventional technologies are improved, and that the applicability of this system is validated. Keywords: Construction Equipment, Excavator, Safety, Proximity Detection, Stereo Vision, Camera DOI: https://doi.org/10.22260/ISARC2013/0110 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".