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