基于跨模态感知与持续学习的数据驱动射频层析成像
Data-driven RF Tomography via Cross-modal Sensing and Continual Learning
AI总结：
本文提出DRIFT框架，通过射频与视觉跨模态感知训练深度神经网络，并结合持续学习自动适应动态环境变化，实现地下根茎横截面重建，平均等效直径误差为2.29厘米，较现有最优方法提升23.2%。
摘要：
数据驱动的射频（RF）层析成像由于射频信号对土壤具有穿透性，已在地下目标检测中展现出显著潜力。然而，在动态环境中实现准确且鲁棒的性能仍然具有挑战性。在本工作中，我们提出了一种数据驱动的射频层析成像（DRIFT）框架，该框架包含以下关键组件，用于在射频信号发生显著变化的情况下重建地下根茎的横截面图像。首先，我们设计了一个包含射频和视觉传感器的跨模态感知系统，并提出按照跨模态学习方法训练射频层析成像深度神经网络（DNN）模型。然后，我们提出应用持续学习，在动态环境中一旦检测到环境变化，就自动更新DNN模型。实验结果表明，我们的方法实现了2.29厘米的平均等效直径误差，相比最先进方法提升了23.2%。我们的DRIFT代码和数据集已在https://github.com/Data-driven-RTI/DRIFT上公开提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
