Target Detection for RD Images of HFSWR Based on CNN-ELM Model
Why this work is in the frame
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Bibliographic record
Abstract
High-frequency surface wave radar (HFSWR) can effectively detect ship targets. However, ship target signals are often affected by strong clutter and complex interference. In this paper, we propose an HFSWR target detection algorithm based on a two-stage cascade detector combined with a convolutional neural network-extreme learning machine (CNN-ELM) model. In the first stage, an extremum detector (ED) is used to obtain suspicious target regions (STRs) in the range-Doppler (RD) spectrum image. In the second stage, a CNN-ELM model is employed. The features of the STRs are learned by a lightweight convolutional neural network (LW-CNN) and fast classification is performed by an extreme learning machine (ELM). The experiments show that the proposed algorithm can achieve better performance in the measured RD image than the traditional detection algorithms.
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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) | 0.000 | 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