A Robust EFPN and CFF-Backbone for Small Object Detection in Visually Degraded Challenging Environments
Bibliographic record
Abstract
Detecting small objects in visually degraded environments presents significant challenges due to occlusions, atmospheric distortions, and complex backgrounds caused by fog, smoke, debris, and adverse lighting. Traditional detection frameworks often fail to localize tiny targets, leading to low detection accuracy and high false positives. We propose a robust deep learning architecture that synergistically combines an Enhanced Feature Pyramid Network (EFPN) with a Cross-Scale Feature Fusion (CFF) backbone. The EFPN module dynamically captures fine-grained semantic and spatial information across multiple scales, while the CFF backbone aggregates details from different image levels, ensuring preservation of minute cues even under severe visual degradation. Attention-driven modules and adaptive fusion layers selectively emphasize relevant features and suppress background noise. Experiments on both synthetic and real-world datasets demonstrate that the proposed method outperforms state-of-the-art detectors in terms of precision, recall, and robustness, especially for tiny and partially occluded objects, showing strong potential for deployment in real-time surveillance, autonomous navigation, and decision support systems in hostile or degraded environments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".