Implementation of the Laplacian of Gaussian Algorithm in Edge Detection Image Processing of Zebra Cross Damage on Highways in the Langkat Regency Area
Bibliographic record
Abstract
Walking is part of the traveler's movement and is the simplest means of transportation, but it is in a weak position and prone to conflict or accidents when they mix with other modes of transportation. To protect pedestrians, special facilities are needed, one of which is a crossing place (zebra crossing) that is able to serve according to pedestrian needs. Based on Law No. 22 of 2009 concerning Traffic Polytechnic Land Transportation Bali 46 Cross and Road Transportation, article 131 paragraph (2), it is stated that "Pedestrians are entitled to priority when crossing the road at the crosswalk". One of the important meanings for human life is the Way. Roads are used as a means of transportation that has a very useful role in efforts to develop human life. In 2018, based on statistical data, the number of motorized vehicle users in Indonesia is increasing every year to reach 146,858,759 units. The impact that occurs is that there are many Zebra Cross roads damaged with conditions that are very troubling and worrying for road users. Among the causes of zebra crossing being damaged will be traffic accidents where the vehicle does not lag obeying the path of the vehicle following the predetermined lane. So this study detects image processing with the Laplacian of Gaussian algorithm with edge detection making it easier for the government to improve traffic signs of zebra crossing images on highways that are worthy of improvement so that accidents do not occur. The results of this study illustrate the image of being able to see damaged zebra crossings with calculations of the Laplacian of Gaussian algorithm.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".