Understanding Strengths and Weaknesses of Complementary Sensor Modalities in Early Fusion for Object Detection
Bibliographic record
Abstract
In object detection for autonomous driving and robotic applications, conventional RGB cameras often fail to sense objects under extreme illumination conditions and on texture-less surfaces, while LIDAR sensors often fail to sense small or thin objects located far from the sensor. For these reasons, an intuitive and obvious choice for perception system designers is to install multiple sensors of different modalities to increase (in theory) the detection robustness. In this paper we focus on the analysis of an object detector that performs early fusion of RGB images and LIDAR 3D points. Our goal is to go beyond the intuition of simply adding more sensor modalities to improve performance, and instead analyze, quantify, and understand the performance differences, strengths and weaknesses of the object detector under three different modalities: 1) RGB-only, 2) LIDAR-only, and 3) Early fusion (RGB and LIDAR), and under two key scene variables: 1) Distance of objects from the sensor (density), and 2) Illumination (Darkness). We also propose methodologies to generate 2D weak semantic training masks, and a methodology to evaluate the object detection performance separately at different distance ranges, which provides a more reliable detection performance measure and correlates well with object LIDAR point density.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".