Lightweight Thermal Super-Resolution and Object Detection for Robust Perception in Adverse Weather Conditions
Bibliographic record
Abstract
In this work, we examine the potential application of thermal cameras in improving perception capabilities in adverse weather conditions like snow, night-time driving, and haze, focusing on retaining the performance of Advanced Driver Assistance Systems (ADAS), thus enhancing its functionality and safety characteristics. While thermal sensors offer the advantage of robust information capture in adverse weather conditions, their integration is plagued with issues surrounding poor feature capture in normal conditions, low imaging resolution, and high sensor costs. We address the former by formulating the problem definition as information switching wherein thermal images are selected when visible images are degraded. Furthermore, we consider a single object detector for RGB and thermal images to ensure low latency. We propose utilizing a learnable projection function that translates the thermal image into RGB color space, thus providing minimal modifications to the underlying object detector. We address the issues of low imaging resolution and cost by proposing a novel procedure that combines super-resolution and object detection, enabling the utilization of low-resolution and low-cost uncooled thermal imaging sensors. To ensure the complete pipeline meets the actual deployment requirements of real-time inference on resource-constrained devices, we introduce a lightweight super-resolution algorithm, implementing optimizations within the network structure followed by global pruning. In addition, to improve the feature representations extracted by lightweight encoders, we propose a bidirectional feature pyramid network to enhance the feature representation. We demonstrate the efficacy of the proposed mechanism through extensive simulated evaluations on automotive datasets such as FLIR, KAIST, DENSE, and Freiburg Thermal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".