Summary of Hardware Acceleration For Real-time Wildfire Detection Onboard Drone Networks, by Austin Briley et al.
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks
by Austin Briley, Fatemeh Afghah
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 This paper presents a real-time image classification and fire segmentation model for autonomous drones used in early wildfire detection. The model is designed to operate efficiently on Unmanned Aerial Vehicles (UAVs) despite their limited computation and battery resources. To achieve this, the researchers utilize hardware acceleration with the Jetson Nano P3450 and NVIDIA’s TensorRT library. They also explore techniques like Quantization Aware Training (QAT), Automatic Mixed Precision (AMP), and post-training mechanisms to optimize fire classification accuracy and speed. The study uses the FLAME dataset, an image dataset collected by low-altitude drones during a prescribed forest fire. The results show a 13% increase in classification speed compared to similar models without hardware optimization, with minimal impact on loss and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us detect wildfires earlier using special flying robots called drones. These drones are important because they can reach remote areas quickly and take good pictures of fires. But the drones don’t have much power or memory, so we need to find a way to make them work better. The researchers in this study tried different ways to speed up their computer programs on the drones. They used special tools like NVIDIA’s TensorRT library and techniques like Quantization Aware Training (QAT) and Automatic Mixed Precision (AMP). They tested these methods using pictures of fires from the FLAME dataset. The results are exciting because they show that we can make the drones work faster without sacrificing too much accuracy. |
Keywords
» Artificial intelligence » Classification » Image classification » Optimization » Precision » Quantization
