Unmanned aerial vehicles (UAVs) are being used for monitoring natural disasters. To promptly assess the severity of the disaster, it is advantageous to analyze the disaster scenes with the on-board computer in real ti...
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ISBN:
(纸本)9798350360332;9798350360325
Unmanned aerial vehicles (UAVs) are being used for monitoring natural disasters. To promptly assess the severity of the disaster, it is advantageous to analyze the disaster scenes with the on-board computer in real time. Various AI segmentation models with high accuracy are developed mainly for offline processing. They require significant memory capacity and computational power. However, on-device AI has the challenge of compressing high-precision models due to the limited memory size and the small computation power. In this research, we develop a lightweight disaster semantic segmentation model for UAV on-device intelligence. From a simple FANet as our baseline, we apply various optimization, such as compact backbone, lightweight attention block, quantization, knowledge distillation, and post processing. With our optimized model, we can reduce 84.2% of the inference time while achieving 0.5% increase in accuracy compared to the baseline model.
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