Two-Stage Target Detection for Compact HFSWR With Space-to-Depth YOLOv8 and Multiframe ViT
Bibliographic record
Abstract
Accurate and reliable target detection is a crucial requirement of high-frequency surface wave radar for effective maritime surveillance. However, existing methods based on single-frame radar images primarily focus on static target features, limiting their ability to capture dynamic ship behaviors across multiple frames. In this study, a two-stage multiframe target detection framework (MFTDF) is proposed to address this issue in ship-target detection from range-Doppler (RD) images. The framework consists of two stages: a space-to-depth YOLOv8 network in Stage-1 and a multiframe vision transformer network in Stage-2. First, Stage-1 aims to extract regions of interest (ROI) from the current and several preceding RD images. Thereafter, target association and pattern selection are applied to collect multiframe image patch sequences for each ROI result. Finally, Stage-2 focuses on further discriminating the input image patches to obtain refined target detection results. Moreover, the dataset for training and validating the two-stage network is automatically generated based on the automatic identification system ship data and constant false alarm rate detection results to identify all true and visible targets, ensuring the dataset's credibility. Experiments using the measured data show that the proposed MFTDF achieves a considerably improved precision rate while maintaining an average improvement of 12.3% in the recall rate. These results confirm that MFTDF delivers superior detection accuracy and efficiency, offering a robust solution for maritime target detection in complex scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".