Temperature Effect on Thermal Imaging and Deep Learning Detection Models
Bibliographic record
Abstract
Infrared cameras can be a great supplement to the environmental perception systems for autonomous driving. Compared to optical cameras, radars, or Lidars, infrared cameras exceed in detecting heat-radiating objects, such as humans and animals, potentially improving the safety of autonomous cars. The underlying detection algorithms for infrared images are generally the same deep learning models applied for optical cameras. However, as the working principles of infrared and optical cameras are different, so are the images they produce. This paper presents the visual differences in infrared images caused by ambient temperature changes and examines their effect on deep learning detectors. Specifically, this study investigates two infrared datasets, one from McMaster University CMHT group and the other from the FLIR company. They represent a northern cold climate and a tropical climate, respectively. Two YOLO-based object detection models are trained on the two datasets separately. The evaluation results show that a colder temperature results in a better performance. In the meantime, cross-evaluation shows a sharp decrease in performance when evaluating the model against the opposite dataset. Furthermore, a third model trained using both datasets outperform the previous two models in all metrics. This study highlights the importance of ambient temperature in training infrared image detectors and provides a feasible solution to performance mismatch issues.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".