Computer Vision for Safe Human-Robot Collaboration in Disassembly: A Systematic Review and Conceptual Framework
Bibliographic record
Abstract
Disassembly, the first stage in remanufacturing, benefits from human-robot collaboration, which improves efficiency and accuracy. ISO 10218-2:2011 mandates human safety, while ISO/TS 15066:2016 emphasizes risk assessments in cobotic systems. This review examines computer vision techniques—object detection, depth sensing, and trajectory planning—for real-time human and cobot arm detection, achieving over 96% accuracy in object recognition and response times under 50 milliseconds, and is robust to environmental factors such as dust. We propose a hybrid approach integrating these techniques to enhance hazard detection, real-time monitoring, and adaptability in HRC disassembly tasks. This framework addresses gaps in dynamic risk assessment, enabling precise hazard identification in unstructured settings and improving human-cobot interaction monitoring. Supported by key performance indicators such as detection accuracy, response time, and adaptability, this approach offers a scalable and robust solution for diverse disassembly scenarios, advancing both safety and efficiency in HRC systems.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".