A Two-Stage Hierarchical One-Class Classification Structure for HFSWR Ship-Target Detection
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Bibliographic record
Abstract
A high-frequency surface wave radar (HFSWR) is an effective tool for monitoring an exclusive economic zone (EEZ). However, the presence of diverse clutters and noises that contaminate the echo signals of the radar hinder its maritime surveillance. To address this issue, this paper presents a two-stage hierarchical one-class classification network (HOCN) designed specifically for ship-target detection in range-Doppler (RD) images. In Stage 1, the plausible region of interest (PROI) is extracted. This stage employs a dynamic threshold optimization strategy and Laplacian kernel to identify the potential regions of interest. In Stage 2, the proposed one-class deconvolutional-and-convolutional network (OC-DCNet) is utilized for fine detection of ship-targets. This stage comprises two sub-modules: the deconvolutional sub-module, which expands the input into a 2D matrix, and the convolutional sub-module, which classifies the input pattern as either a ship-target or a non-ship-target. The experimental results on a newly collected dataset called HFRD demonstrate the effectiveness of the proposed HFSWR ship-target detection algorithm.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it