Discriminating man-made and natural objects in sidescan sonar imagery: human versus computer recognition performance
Bibliographic record
Abstract
Sidescan sonar is increasingly accepted as the sensor of choice for sea minehunting over large areas in shallow water. Automatic Target Recognition (ATR) algorithms are therefore being developed to assist and, in the case of autonomous vehicles, even replace the human operator as the primary recognition agent deciding whether an object in the sonar imagery is a mine or simply benign seafloor clutter. Whether ATR aids or replaces a human operator, a natural benchmark for judging the quality of ATR is the unaided human performance when ATR is not used. The benchmark can help when estimating the performance benefit (or cost) of switching from human to automatic recognition for instance, or when planning how human and machine should best interact in cooperative search operations. This paper reports a human performance study using a large library of real sonar images collected for the development and testing of ATR algorithms. The library features 234 mine-like man-made objects deployed for the purpose, as well as 105 instances of naturally occurring clutter. The human benchmark in this case is the average of ten human subjects expressed in terms of a receiver operating characteristic (ROC) curve. An ATR algorithm for man-made/natural object discrimination is also tested and compared with the human benchmark . The implications of its relative performance for the integration of ATR are considered.
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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 | 0.001 | 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 | 0.001 |
| 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".