Real-time traffic sign detection network based on Swin Transformer
Bibliographic record
Abstract
Abstract In the field of autonomous driving, the detection of traffic signs remains a significant challenge, especially when it comes to the real-time detection of medium and small targets. The difficulty of detecting small objects decreases accuracy. To address these challenges, we propose a real-time traffic sign detection algorithm based on the Swin Transformer (RTSDST) that improves computation performance and accuracy for multi-scale target detection on SoCs installed onboard autonomous driving vehicles. Our approach includes a head specifically designed for detecting tiny objects, followed by the adoption of Swin Transformer blocks to effectively capture the spatial and channel dependencies of the feature maps, which improves the accuracy of detecting targets of varying sizes. To efficiently identify regions of interest in large coverage images, we employ a Residual Convolutional Attention Module to generate sequential feature maps between the channel and spatial dimensions and weigh them against the original map. A realistic traffic sign detection dataset, Tsinghua-Tencent 100K (TT100K), which includes medium and small traffic sign targets, was adopted in this article to evaluate the effectiveness of our proposed RTSDST. The evaluation results show that RTSDST has excellent performance on multi-scale scenes. Additionally, we also evaluated our network on the VisDrone dataset for small target detection. Our method has state-of-art performance on small targets.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".