Using convolutional neural networks on perceptual and spectral features: Classification experiments on the TREX’13 dataset
Bibliographic record
Abstract
Distinguishing objects of interest on the seafloor from clutter remains a key problem facing the automatic target recognition (ATR) community. Because scattering at high frequencies relates more to the geometry of the scatterer, traditional ATR on high frequency imaging sonars is known to suffer in areas of high clutter. Recently, there has been interest in low frequency (1–50 kHz) wideband imaging sonars, because the complex combination of external (geometric) and internal (elastic) scattering that occurs in this frequency range is thought to improve classification in areas of high clutter. This work investigates the classification problem of distinguishing unexploded ordnance (UXO) from clutter using image-based Convolutional Neural Networks on the TREX’13 dataset. This dataset consists of experimental and modelled acoustic backscatter for objects interrogated acoustically at 3–30 kHz across a range of aspects. The model data are used exclusively for training, and the experimental data are used exclusively for testing purposes. The generated feature-set is a combination of acoustic colour and perceptual features, which are derived from modelling how humans perceive timbre. Further in an effort to address the differing amplitude modulations between sets of model and experimental data and improve classified performance, several moving window normalizations will be investigated.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".