SegAttnDetec: A Segmentation-Aware Attention-Based Object Detector
Bibliographic record
Abstract
Object detection (OD) has emerged as a cornerstone of computer vision applications, with deep learning (DL) driving significant advancements. While modern OD algorithms excel in identifying objects, they often falter when confronted with small objects in intricate scenes. To address this challenge, we introduce SegAttnDetec, a novel framework that leverages semantic segmentation to enhance object detection performance. By fusing semantic segmentation-aware features with the backbone of an OD model and incorporating an attention-gating mechanism, SegAttnDetec enables the model to capture richer, more refined features, leading to substantial improvements in small object detection. Notably, our approach achieves a remarkable 28.5% increase in pedestrian hard category detection and a 38.8% improvement in cyclist hard category detection on the KITTI benchmark dataset; hence, reaching an overall mean average precision (mAP) of 83.5% in all the categories of the same dataset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".