Feature extraction and target classification of side-scan sonar images
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
Side-scan sonar technology has been used over the last three decades for underwater surveying and imaging. Application areas of side-scan sonar include archaeology, security and defence, seabed classification, and environmental surveying. In recent years the use of autonomous underwater systems has allowed for automatic collection of data. Along with automatic collection of data comes the need to automatically detect what information is important. Automatic target recognition can allow for efficient task planning and autonomous system deployment for security and defence applications. Support Vector Machines (SVMs) are proven general purpose methods for pattern classification. They provide maximum margin classification that does not over fit to training data. It is generally accepted that the choice of kernel function allows for domain specific information to be leveraged in the classification system. In this paper it is shown that for target classification in side-scan sonar, extra feature extraction and data engineering can result in better classification performance compared to parameter optimization alone.
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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) | 0.001 | 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