Enhanced Thermal-RGB Fusion for Robust Object Detection
Why this work is in the frame
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Bibliographic record
Abstract
Thermal imaging has seen rapid development in the last few years due to its robustness in different weather and lighting conditions and its reduced production cost. In this paper, we study the performance of different RGB-Thermal fusion methods in the task of object detection, and introduce a new RGB-Thermal fusion approach that enhances the performance by up to 9% using a sigmoid-activated gating mechanism for early fusion. We conduct our experiments on an enhanced version of the City Scene RGB-Thermal MOT Dataset where we register the RGB and corresponding thermal images in order to conduct fusion experiments. Finally, we benchmark the speed of our proposed fusion method and show that it adds negligible overhead to the model processing time. Our work would be useful for autonomous systems and any multi-model machine vision system. The improved version of the dataset, our trained models, and source code are available at https://github.com/wassimea/rgb-thermalfusion.
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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 | 0.000 |
| 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 it