Deep Saliency With Channel-Wise Hierarchical Feature Responses for Traffic Sign Detection
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
Traffic sign detection is challenging in cases of a complex background, occlusions, distortions, and so on. To overcome the above-mentioned challenges, this paper pays close attention to channel-wise feature responses to propose an end-to-end deep learning-based saliency traffic sign detection method. Our model contains three main components: channel-wise coarse feature extraction (CCFE), channel-wise hierarchical feature refinement (CHFR), and hierarchical feature map fusion (HFMF). In addition, it is based on the squeeze-and-excitation-residual network to explicitly model the inter dependences between the channels of its convolution features at a slight computational cost. We first apply CCFE to produce coarse feature maps with much information loss. To make full use of spatial information and fine details, CHFR is executed to refine hierarchical features. After that, HFMF is used to fuse hierarchical feature maps to generate the final traffic sign saliency map. Compared with other five traffic sign detection methods, the experimental results demonstrate the efficiency (a real-time speed) and superior performance of the proposed method according to comprehensive evaluations over three benchmark data sets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it