A softmax classifier for high-precision classification of ultrasonic similar signals

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.