Pedestrian and Cyclist Object Detection Using Thermal and Dash Cameras in Different Weather Conditions
Bibliographic record
Abstract
Ensuring the safety of cyclists and pedestrians has become imperative in our ever expanding urban centers. Despite advancements in vehicle safety technology, traditional cameras often fail in adverse weather and low-light conditions. This paper investigates the efficiency of integrating thermal cameras with dash cameras to enhance detection accuracy of vulnerable road users. We first collected and annotated datasets, comprising thermal and dash camera footage under various weather conditions. We then developed a deep learning object detection model using YOLOv8 and Roboflow. Separate models were trained for each camera, then fused to compensate for their individual limitations. It was observed that dash camera is prone to occlusions and varied lighting, whereas the thermal camera excels in low-light settings. The performance metrics for the thermal camera showed a total mAP50 of 0.92 and mAP50-95 of 0.52 for detecting both cyclists and pedestrians, reflecting a highly effective system with significant potential to improve road safety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".