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.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".