Bibliographic record
Abstract
In the rapidly advancing field of autonomous technologies, particularly in transportation and surveillance, there is a critical need for accurate and reliable pedestrian detection systems. While deep learning models have achieved remarkable success in this area, their “black box” nature often concerns their trustworthiness and practical applicability. Our study introduces a modified YOLOv5s model, augmented with a Global Attention Mechanism and enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM) for improved explainability. This approach maintains the high accuracy typical of deep learning models while significantly increasing their transparency. Our modified YOLOv5s architecture is tailored for real-time applications, achieving an inference speed of 9.4 milliseconds on a single NVIDIA A100 GPU. Incorporating Grad-CAM provides a more intuitive interpretation of the model's decisions, potentially increasing user confidence in its predictions. We evaluate our enhanced framework on the CityPersons dataset, demonstrating improvements in precision, recall, and mAP@0.5 compared to the baseline YOLOv5s model. Our results indicate a promising advancement in pedestrian detection, balancing high performance with improved explainability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".