Crack image detection based on fractional differential and fractal dimension
Why this work is in the frame
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Bibliographic record
Abstract
In civil engineering, crack detection using image processing has gained much attention among researchers and transportation agencies. As the crack image often presents a fuzzy boundary and random shape, it is difficult to achieve satisfactory detection performance. This study proposes a crack detection method based on the fractional differential and fractal dimension. This method achieves image enhancement and crack extraction in two stages. First, an image enhancement algorithm based on the fractional differential is applied to solve the fuzzy crack boundary. This algorithm can enhance the crack boundary information significantly while simultaneously maintaining texture details. Second, an improved extraction algorithm based on the fractal dimension is studied. This algorithm can effectively accomplish crack extraction according to shape features. Last, upon comparisons with classic and state‐of‐the‐art methods, the experiment shows that the proposed method can achieve satisfactory results for crack image detection.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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