A pulse length tolerant neural network-based detector for sector-scan sonar
Bibliographic record
Abstract
In this paper we present a neural network-based system to detect small manmade objects in sequences of sector-scan sonar images created using signals of various pulse lengths. The sonar system considered has three modes of operation to create images over ranges of up to 800 m using acoustic pulses of different durations for each mode. After initial cleaning and segmentation to extract objects, features are computed from each object. These features consist of basic object size and contrast statistics, shape moments, moment invariants, and features derived from the second-order histogram of each object. Optimal sets of 15 features from the total set of 31 are chosen using sequential feature selection techniques. Using these features a neural network is trained to detect manmade objects in any of the three sonar modes. The proposed detector is shown to perform very well when compared with detectors trained specifically for each sonar mode and a number of statistical detectors. The proposed detector achieves a 92.4% detection probability at a mean false alarm rate of 10 per frame averaged over all sonar mode settings. Finally, research into Recurrent Neural Network detectors is described and shown to further improve performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".