metadata
title: CEA List FrugalAI Challenge
emoji: 🔥
colorFrom: red
colorTo: yellow
sdk: docker
pinned: false
license: apache-2.0
short_description: YOLO for low-emission Early Fire Detection
YOLO for Early Fire Detection
Team (CEA List, LVA)
- Renato Sortino
- Aboubacar Tuo
- Charles Villard
- Nicolas Allezard
- Nicolas Granger
- Angélique Loesch
- Quoc-Cuong Pham
Model Description
YOLO model for early fire detection in forests, proposed as a solution for the Frugal AI Challenge 2025, image task.
Training Data
The model uses the following datasets:
| Dataset | Number of samples | Number of instances |
|---|---|---|
| pyronear/pyro-sdis | 29,537 | 28,167 |
| D-Fire | 10,525 | 11,865 |
| Wildfire Smoke Dataset | ~12,300 | 11,539 |
| Hard Negatives | ~5,000 | ~5,000 |
| Synthetic Dataset | ~5,000 | ~5,000 |
Performance
Model Architecture
The model is a YOLO-based object detection model, that does not depend on NMS in inference. Bypassing this operation allows for further optimization at inference time via tensor decomposition.
Metrics
| Model | Accuracy | Precision | Recall | meanIoU | Wh | gCO2eq |
|---|---|---|---|---|---|---|
| YOLOv10s | 0.87 | 0.88 | 0.98 | 0.84 | 6.77 | 0.94 |
| YOLOv10m | 0.88 | 0.87 | 0.99 | 0.88 | 8.39 | 1.16 |
| YOLOv10m + Spatial-SVD | 0.85 | 0.86 | 0.97 | 0.82 | 8.24 | 1.14 |
Environmental impact is tracked using CodeCarbon, measuring:
- Carbon emissions during inference (gCO2eq)
- Energy consumption during inference (Wh)
This tracking helps establish a baseline for the environmental impact of model deployment and inference.
Limitations and future work
- It may fail to generalize to night scenes or foggy settings
- It is subject to false detections, especially at low confidence thresholds
- Clouds at ground level can be misinterpreted as smoke
- It would be better to use temporal-aware models trained on videos