High-resolution Ultrasonic Echo Detection with Two-stage Recurrent Neural Network

Ultrasonic echo methods have been widely researched for the application of flaw detection, where the flaw locations are identified by the arrival time of each echo. The main difficulty is that the receiving echoes from consecutive flaws overlap in time when the flaws are close. Over the last decades, sparse approximation and neural-network-based methods have been used to address this issue. However, these methods cannot achieve satisfactory performance in high-noise and severe overlapping scenarios. In this paper, we propose a high-resolution ultrasonic echo detection method with two-stage recurrent neural network, which includes the localization of echo and the regression of echo amplitude. The proposed method adopts two-stage ideology which filters interfering sequences through localization and predicts the amplitude of echo. The proposed method realizes high-resolution ultrasonic echo detection and performs well under severe overlapping, while it improves greatly the detection speed.