AN OBSTACLE DETECTION SYSTEM BASED ON MONOCULAR VISION FOR APPLE ORCHARD ROBOT
Bibliographic record
Abstract
Obstacle detection is one of the key problems in the autonomous movement of intelligent mobile robot.This paper focuses on the study of obstacle detection in the complex apple orchard.First, using L * a * b * (colour space for non-self-illuminated, named by CIE 1976 L * a * b * or CIELAB) colour model to represent the apple orchard image, the obstacles in the robot's walking area can be effectively segmented from the complex background by onedimensional fuzzy entropy image segmentation algorithm.Then, the coordinate of the point close to the robot of the obstacle is obtained.Finally, the distance between the robot and the obstacle is estimated using the inverse perspective transformation.The experimental results show that the maximum absolute error is 7.87 cm and the maximum relative error is 6.53%.The results can meet the accuracy requirements of the intelligent mobile application for an apple orchard 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".