Indoor FireRescue Radar: 4D Indoor Millimeter Wave Dataset and Analysis for Hazardous Environment Perception
Bibliographic record
Abstract
Fire-induced indoor environments, characterized by smoke, glare, and dimness, critically challenge rescue safety. While LiDAR and cameras suffer from signal attenuation, millimeter-wave (mmWave) radar exhibits robust imaging performance. Radar-based building mapping and object detection in indoor environments are thus required to facilitate situational awareness specified by firefighting standards. Prior radar datasets mostly focus on outdoor object detection and the few existing indoor datasets remain insufficient in several aspects: (1) lacking adverse scenario analysis; (2) lacking raw analog-to-digital converter (ADC) data for dense point cloud generation; and (3) lacking 3D object annotations for building layout understanding. This work introduces the Indoor FireRescue Radar (IFR) dataset, a novel large-scale multimodal benchmark for indoor situational awareness. It includes 27K frames of 4D radar point cloud, co-calibrated with LiDAR, RGB camera, and IMU streams, alongside 3D objects annotations across 10 buildings. This dataset also provides raw ADC data and sensor configuration metadata. We applied voxel-based and pillar-based object detectors to 4D radar-based indoor object detection. We also demonstrated the robustness of radar perception in fire-induced indoor environments by real smoke tests at a firefighter training facility. Dataset is available at: https://huggingface.co/datasets/yysd123/indoor_mmwave
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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) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.010 |
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".