Abstract Robotic vision in a regenerating forest environment
Bibliographic record
Abstract
The Canadian Forest Service has been investigating the potential of low-cost autonomous roving robots to perform repetitive stand-tending tasks that could improve forest productivity. A first prototype, Jacob, should function much like a person with a brush saw, freeing young conifers from some of the competing vegetation. Following a brief description of Jacob, its task, and its general sensing needs, some of the specific vision situations (day, night, season, closeness to target) the robot will likely encounter are outlined. Conceptual approaches to solving each situation are suggested. Specific techniques tailored to each situation are developed. Here, three image analysis techniques based respectively on colour, structure and directionality are described. Results are presented with comments on potential effectiveness, limitations and operational constraints of each technique. In addition, a simple stereoscopic object-matching scheme used to calculate the distance from the robot to the recognized objects is described. Following their implementation, integration and testing as part of Jacob’s control system, it is hoped that these and other computer vision techniques will form a sufficient basis to further the development of an autonomous silviculture robot.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".