Small Target Detection Methods in Complex Scenes
Why this work is in the frame
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Bibliographic record
Abstract
Small target detection is of great significance in military and civilian fields, so the problems of small target aggregation and scale diversity under scene complexity, this paper proposes a small target detection method under complex scenes for small target aggregation and scale diversity under scene complexity. Firstly, we design a multiscale grouping efficient convolution module, Multi-ScaleGroupEfficientConv, to replace part of the C2f module, so as to extract multiscale features efficiently; secondly, we construct a feature pyramid network for small targets and construct a CSP-OmniKernel feature fusion module based on the idea of CSP and OmniKernel to retain the information of small targets to a greater extent. The intent is to retain the information of small targets and improve the target detection ability. Finally, on the VisDrone2019 dataset, comparison experiments are conducted with mainstream algorithms, and the experimental results show that the method in this paper improves 1.2%, 2.1%, 2.6%, and 1.3% in the detection rate, recall rate, mAP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf>, and mAP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50:95</inf>, respectively, in comparison with the original yolov8n, and the model size and the number of parameters are only 6.1MB and 3.02M, which are better than other mainstream methods.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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