Yolov8s-Tsd: A traffic sign detection model based on feature extraction and feature enhancement
Bibliographic record
Abstract
Aiming at the challenges of low detection accuracy and missed detections caused by complex backgrounds and densely distributed small traffic signs, this paper proposes YOLOv8s-TSD, a traffic sign detection network that incorporates a multi-branch feature extraction module and multi-scale feature fusion. First, an Efficient Feature Extraction Module (EFEM) is designed by integrating multi-branch feature extraction with GhostNet to fully extract shallow features while maintaining high inference speed. Second, a Feature Enhancement Module (FEM) is introduced to enhance the highest-level features and improve the perception of small-scale objects. Finally, an additional detection head specifically targeting small objects is incorporated. Test results on the TT100K dataset demonstrate that YOLOv8s-TSD achieves 2.9% improvement in mAP@0.5 compared to YOLOv8s, proving the effectiveness of the network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".