High precision classification of ultrasonic signals is helpful to improve the identification and evaluation accuracy for detecting defects. In the previous research, the deep neural network (DNN) has been used to classify the signal with obvious differences. But for different defects of the same depth, or when the defect position is close, the ultrasonic A-scan signal curve is very similar, causing the classification accuracy not high enough. In this paper, an optimized softmax classifier is proposed based on the traditional softmax classifier, and the convolution neural network (CNN) framework is built, which can achieve the accurate classification of signals with similar curves. Through a comparative experiment, the performance of the proposed classifier is evaluated from the loss curve decline rate, classification accuracy and feature visualization. The results show that the classifier has high classification accuracy and strong robustness.

中文翻译：

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一种用于超声波相似信号高精度分类的 Softmax 分类器

超声波信号的高精度分类有助于提高检测缺陷的识别和评价精度。在以往的研究中，深度神经网络（DNN）已经被用于对具有明显差异的信号进行分类。但对于相同深度的不同缺陷，或缺陷位置较近时，超声A扫描信号曲线非常相似，导致分类精度不够高。本文在传统的softmax分类器的基础上提出了一种优化的softmax分类器，并构建了卷积神经网络（CNN）框架，可以实现对相似曲线信号的准确分类。通过对比实验，从损失曲线下降率来评价提出的分类器的性能，分类精度和特征可视化。结果表明，该分类器具有较高的分类准确率和较强的鲁棒性。