论文标题
使用卷积神经网络从声学散射中预测物体几何形状
Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks
论文作者
论文摘要
声学散射受声音散射的物体边界几何形状的强烈影响。目前的工作提出了一种通过训练卷积神经网络从散射特征中推断出对象几何形状的方法。训练数据是由在CUDA上开发的快速数值求解器生成的。对完整的模拟集进行了采样，以生成包含不同量的通道和各种图像分辨率的多个数据集。通过比较使用数据降解级别的数据集进行训练的网络的性能，可以评估我们方法对数据降解的鲁棒性。目前的工作发现，我们模型的预测与地面真理相匹配。此外，当使用较少的数据通道或较低的分辨率时，准确性不会降低。
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.
