Robust Horizon Detection Using Segmentation for UAV Applications
Bibliographic record
Abstract
A critical step in navigation of unmanned aerial vehicles is the detection of the horizon line. This information can be used for adjusting flight parameters as well as obstacle avoidance. In this paper, a fast and robust technique for precise detection of the horizon path is proposed. The method is based on existence of a unique light field that occurs in imagery where the horizon is viewed. This light field exists in different scenes including sea-sky, soil-sky, and forest-sky horizon lines. Our proposed approach employs segmentation of the scene and subsequent analysis of the image segments for extraction of the mentioned field and thus the horizon path. Through various experiments carried out on our own dataset and that of another previously published paper, we illustrate the significance and accuracy of this technique for various types of terrains from water to ground, and even snow-covered ground. Finally, it is shown that robust performance and accuracy, speed, and extraction of the path as curves (as opposed to a straight line which is resulted from many other approaches) are the benefits of our method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".