A Lighter and Faster One-Stage Algorithm for Object Detection in Remote Sensing Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Remote sensing images processing and analysis face significant challenges due to varying object scales and complex backgrounds. Existing detection algorithms often suffer from high computational complexity and suboptimal performance. A lightweight algorithm SCC-YOLO was proposed for remote sensing objects detection. It incorporates three key innovations: (1) Slimneck-V feature fusion architecture to enhance multi-scale adaptability while reducing computational load. (2) Cross Stage Partial with Context Anchor Attention (C2CAA) module to improve feature representation of key object regions. (3) Cross Stage Partial with Ghost (CSPGhost) module that optimizes feature extraction efficiency. The algorithm is validated on DOTA and RSOD datasets. Experimental results demonstrate that, compared to baseline algorithms, SCC-YOLO reduces model parameters by 15.3% and computational complexity by 26%. On the DOTA dataset, detection accuracy and inference speed are improved by 3.9% and 6.5%, respectively.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it