E2 Net: Efficient and Effective Dense Pedestrian Detection Network Based on YOLOv8
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
The goal of dense pedestrian detection is to accurately identify and locate pedestrians in crowded scenes. The majority of available dense pedestrian detection algorithms are built on a two-stage framework. The two-stage framework generally transforms the target detection task into a regression task by selecting candidate regions. However, two-stage-based approaches have issues with high computational complexity and subpar real-time performance since they necessitate several region suggestions and feature extraction operations. By executing prediction and regression operations directly on the feature map and skipping region suggestion and multi-stage processing, YOLOv8, as a single-stage detection approach, may substantially decrease computational complexity and increase real-time performance. However, it still has shortcomings in small-scale pedestrian detection and occlusion processing. To solve this problem, we propose an efficient and effective dense pedestrian detection method based on YOLOv8, called E2 Net. We introduce an efficient convolution operator, Partial Convolution (PConv), to reduce computational redundancy and memory consumption. Also, we apply PConv to the FasterNet architecture to improve feature extraction efficiency while maintaining performance, enabling efficient spatial feature extraction on multiple devices. In addition, we introduce a novel loss optimization scheme to reduce small-scale pedestrian misses and incorporate a weighted bi-directional feature pyramid network (BiFPN) to achieve a flexible multi-scale feature fusion algorithm with content awareness. Through extensive experiments, it has been verified that E2 Net has higher accuracy and efficiency on dense pedestrian detection tasks than existing state-of-the-art algorithms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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