Dynamic display algorithm of sonar data based on grayscale distribution model and computational intelligence
Bibliographic record
Abstract
Existing image processing and target recognition algorithms have limitations in complex underwater environments and dynamic changes, making it difficult to ensure real-time and precision. Multiple noise sources interfere with sonar signals, which affects both data precision and clarity. This article studies the dynamic display algorithm of sonar data based on grayscale distribution model and computational intelligence. It proposes to construct a grayscale distribution model for sonar images, analyze the grayscale histogram, determine the threshold selection of the maximum entropy threshold segmentation method, and finally complete the target segmentation. The segmented images can be used to train the convolutional neural network object recognition model constructed in this article. To verify the effectiveness of the proposed method, a test set was used to evaluate the trained target recognition model. The precision of the model recognition was 87.95%, the recall was 87.97%, and the F1 value was 0.8794, which is significantly higher than the traditional model (Such as Otsu and SVM is below 80%). The recognition speed reached 37 m, which is a certain improvement compared to the traditional model.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".