A Lighter and Faster One-Stage Algorithm for Object Detection in Remote Sensing Images
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".