Cross-Scale Feature Enhancement for Cotton Seedling Detection in UAV Images
Bibliographic record
Abstract
Deep-learning-based object detection methods have achieved significant results in unmanned aerial vehicle (UAV) crop seedling image detection. However, when there are differences in the shape characteristics and sizes of seedlings within datasets, the performance of the detector tends to decrease. Existing methods typically rely on specific datasets, ignoring the problem of feature disparities caused by complex and variable field environments. In this letter, a cotton seedling detection framework based on cross-scale feature enhancement (CFE) is presented. CFE reconstructs features through multilevel feature aggregation (MFA) and enhances the reconstructed feature layers using global contextual dependencies extracted by transformer encoder, enabling the sharing of long-range dependency information across different feature spaces. Furthermore, a fuzzy dynamic weighted loss (FDWLoss) strategy is proposed to balance the targets for difficult-to-identify in the training process. Experimental results demonstrate a significant improvement in detection performance and generalization ability on six datasets of the proposed model, which is particularly suitable for cotton seedling detection in various field environments.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".