Beyond Object Detection with Existence Maps for Anchor-Based Deep Learning Models
Bibliographic record
Abstract
LiDAR-based object detection models have achieved impressive accuracy in autonomous driving benchmarks. However, despite improvements in efficiency, these models lack interpretability and measured accuracies are heavily dataset-dependent, reflecting closed-set performance. Further, in the real-world, many dynamic situations arise, inducing inevitable perception errors. To address these limitations, we propose a twofold approach: first, we introduce the Existence Map, a method to visualise the internal knowledge of deep learning models that suggests existence of objects, and second, we propose a methodology to merge this information with the standard output, supplementing detections and calibrating final confidences, reducing the mean absolute error in 12.3%. Further, our experiments on the KITTI dataset demonstrate that the merging strategy can enhance precision and recall by 0.03 and 0.04, respectively, when evaluated across all ground-truth classes despite the model being trained on only cars, pedestrians and cyclists. Additionally, we show that existence maps can help identify missed objects, reduce false positives, and capture location uncertainties, leading to improved performance and increased interpretability in safety-critical object detection applications.
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".