Summary of Detecting Wildfires on Uavs with Real-time Segmentation Trained by Larger Teacher Models, By Julius Pesonen et al.
Detecting Wildfires on UAVs with Real-time Segmentation Trained by Larger Teacher Models
by Julius Pesonen, Teemu Hakala, Väinö Karjalainen, Niko Koivumäki, Lauri Markelin, Anna-Maria Raita-Hakola, Juha Suomalainen, Ilkka Pölönen, Eija Honkavaara
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method for wildfire smoke segmentation on UAVs uses small specialized models trained with bounding box labels and zero-shot foundation model supervision. This approach allows for real-time recognition of smoke on a NVIDIA Jetson Orin NX computer, achieving 63.3% mIoU on a manually annotated dataset. The model’s performance is demonstrated at real-world forest burning events. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wildfires can cause huge damage to the environment and communities. To stop big fires, it’s important to detect them early. One way to do this is with special drones called UAVs that have cameras and computers on board. These drones can cover a lot of ground quickly without needing much infrastructure. However, in areas where there are no strong internet connections, the drones need to process images themselves to detect smoke. The problem is that we don’t have enough training data for deep learning models to recognize wildfire smoke. This study shows how small AI models can be trained using only simple labels and help with real-time smoke detection. |
Keywords
» Artificial intelligence » Bounding box » Deep learning » Zero shot
