Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks
Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks
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DOI：
10.1109/icassp39728.2021.9414743
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Ziqi Fan;Vibhav Vineet;Chenshen Lu;K. McMullen
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文献类型：
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作者：
Ziqi Fan;Vibhav Vineet;Chenshen Lu;K. McMullen
声散射受声音散射的物体的边界几何形状的强烈影响。本文提出了一种通过训练卷积神经网络从散射特征中推断物体几何形状的方法。训练数据由基于CUDA开发的快速数值求解器生成。完整的模拟集被采样以生成包含不同数量通道和不同图像分辨率的多个数据集。通过比较使用不同数据退化水平的数据集训练的网络的性能，我们的方法在响应数据退化方面的鲁棒性得到了评估。目前的工作已经发现，我们的模型所做的预测与实际情况相符，准确度很高。此外，当使用较少的数据通道或较低的分辨率时，精度不会降低。
Acoustic scattering is strongly influenced by boundary geometry of objects over which sound scatters. The present work proposes a method to infer object geometry from scattering features by training convolutional neural networks. The training data is generated from a fast numerical solver developed on CUDA. The complete set of simulations is sampled to generate multiple datasets containing different amounts of channels and diverse image resolutions. The robustness of our approach in response to data degradation is evaluated by comparing the performance of networks trained using the datasets with varying levels of data degradation. The present work has found that the predictions made from our models match ground truth with high accuracy. In addition, accuracy does not degrade when fewer data channels or lower resolutions are used.