Image Processing in Repairing the Red Zone of Vehicle Barriers in Binjai City with Edge Detection Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Red zones on road markings are an important element in the traffic system that serves as a barrier or prohibition on stopping, parking, or crossing certain areas. In Binjai City, red zones are commonly found at intersections, near zebra crosses, or busy areas such as markets and schools. However, in its implementation in the field, the effectiveness of red zones is often not optimal due to various obstacles. In addition, "manual surveillance of red zone conditions" requires large human resources and has not been able to reach all vulnerable points effectively. Regular checks and maintenance efforts are often hampered by time and budget constraints. As a result, some red zone points are damaged or lost unnoticed for a long time. This study aims to design and test image processing methods with edge detection algorithms in detecting and improving the appearance of traffic red zones in Binjai City. It is hoped that this solution can increase the effectiveness of traffic supervision and support efforts to control highways in a more modern and efficient manner. The result of the calculation above is a binner image with the number 0 being the color that shows black and the number 1 is the color that shows white. Showing the image is the result of a black and white image process. So from the calculation above, there is a Sobel algorithm that calculates the final value of the higher calculation is the Sobel algorithm with the level of fineness and clarity in the image.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it