A Deep Learning-Based Time-Frequency Scheme for Ship Detection Using HFSWR
Bibliographic record
Abstract
Compact High frequency surface wave radar (HFSWR) has been widely used in remote sensing of oceanic dynamics and ship targets due to its convenient deployment and low cost. However, when using a constant false alarm rate (CFAR) detector, these systems experience performance degradation primarily because of echo nonstationarity. To address this challenge, a deep learning (DL)-based scheme tailored for identifying ship targets in the time-frequency (TF) domain is presented. To ensure high-quality model training, we develop a semiautomatic annotation approach that uses automatic identification system (AIS) information as a reference and collect a TF dataset named HFSWR-TFD. In addition, inspired by the dynamic snake convolution and triplet attention mechanism, an improved YOLOv5s model named DS-YOLOv5s is designed to effectively capture target ridges. The inference results are filtered using a confidence threshold and then transformed into the range-Doppler domain for final target identification. Experimental results on the newly collected dataset show significant improvements are achieved by DS-YOLOv5s. Compared to its baseline, the DS-YOLOv5s can increase the F1 score by 15.3%, and AP75 by 6.3%. Then, this pretrained DL model is integrated into the entire scheme to make comparison with existing CFAR detectors. With the AIS records as ground truth, our scheme achieves a match rate that is 2.27$\sim$8.17% greater than its CFAR counterparts. Moreover, the quantitative results of the associate tracks further confirm the superiority of the proposed method. In conclusion, the proposed scheme provides an effective and efficient solution for HFSWR ship detection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".