Enhancing Object Detection in Dense Images: Adjustable Non-Maximum Suppression for Single-Class 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
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.
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 | 0.000 | 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