Enhancing Object Detection in Dense Images: Adjustable Non-Maximum Suppression for Single-Class Detection
Bibliographic record
Abstract
Deep learning-based object detection technology often relies on non-maximum suppression (NMS) algorithms to eliminate redundant detections. However, the conventional NMS algorithm struggles with distinguishing between overlapping and small objects due to its simple constraints. While Soft-NMS offers a slight improvement in object detection performance, it still falls short in addressing this challenge. Our proposed solution, adjustable-NMS, represents a significant advancement. While performing comparably to NMS and Soft-NMS on less dense images where objects are easily countable, adjustable-NMS excels in scenarios with higher object density or smaller objects. In such cases, it outperforms both NMS and Soft-NMS, showcasing notably superior object detection capabilities. On average, the improvement achieved with adjustable-NMS reaches an impressive 33.3%. This demonstrates adjustable-NMS’s efficacy in enhancing object detection accuracy, particularly in challenging environments characterized by dense scenes or diminutive objects.
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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 | 0.002 | 0.005 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".