用雷达与视觉融合数据，动态更新模型，精准重建地下根茎图像
Data-driven RF Tomography via Cross-modal Sensing and Continual Learning
- 融合雷达与视觉数据，通过跨模态学习训练深度网络
- 在动态环境中自动更新模型，实现2.29厘米平均误差
- 适合地下作物检测、智能农业等需要持续适应的场景
数据驱动的射频断层成像（DRIFT）在穿透土壤探测地下目标方面展现出巨大潜力。然而，在动态环境中实现准确且鲁棒的性能仍具挑战。本文提出一种结合跨模态感知与持续学习的数据驱动射频断层成像框架，用于重建地下根茎的横截面图像，即使在射频信号显著变化时亦可保持稳定。首先，设计了集成射频与视觉传感器的跨模态感知系统，并采用跨模态学习方法训练射频断层成像深度神经网络（DNN）模型。其次，引入持续学习机制，在检测到环境变化时自动更新模型。实验结果表明，该方法平均等效直径误差为2.29厘米，相较于当前最优方法提升23.2%。本文开源代码与数据集已发布于https://github.com/Data-driven-RTI/DRIFT。
原文摘要 · Abstract (English)
Data-driven radio frequency (RF) tomography has demonstrated significant potential for underground target detection, due to the penetrative nature of RF signals through soil. However, it is still challenging to achieve accurate and robust performance in dynamic environments. In this work, we propose a data-driven radio frequency tomography (DRIFT) framework with the following key components to reconstruct cross section images of underground root tubers, even with significant changes in RF signals. First, we design a cross-modal sensing system with RF and visual sensors, and propose to train an RF tomography deep neural network (DNN) model following the cross-modal learning approach. Then we propose to apply continual learning to automatically update the DNN model, once environment changes are detected in a dynamic environment. Experimental results show that our approach achieves an average equivalent diameter error of 2.29 cm, 23.2% improvement upon the state-of-the-art approach. Our DRIFT code and dataset are publicly available on https://github.com/Data-driven-RTI/DRIFT.
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