Ultrasonic Pulse-Echo techniques have a significant role in monitoring the integrity of layered structures and adhesive joints along their service life. However, when acoustically measuring thin layers, the resulting echoes from two successive interfaces overlap in time, limiting the resolution that can be resolved using conventional pulse-echo techniques. Deep convolutional networks have arisen as a promising framework, providing state-of-the-art performance for various signal processing tasks. In this paper, we explore the applicability of deep networks for detection of overlapping ultrasonic echoes. The network is shown to outperform traditional algorithms in simulations for a significant range of echo overlaps, echo pattern variance and noise levels. In addition, experiments on two physical phantoms are conducted, demonstrating superiority of the network over traditional methods for layer thickness estimation.

超声脉冲回波技术在监测分层结构和胶粘剂接头在其使用寿命期间的完整性方面具有重要作用。然而，当对薄层进行声学测量时，来自两个连续界面的回波在时间上重叠，限制了使用传统脉冲回波技术可以解决的分辨率。深度卷积网络已成为一种很有前途的框架，可为各种信号处理任务提供最先进的性能。在本文中，我们探索了深度网络在检测重叠超声回波方面的适用性。该网络在模拟中表现出优于传统算法的回声重叠、回声模式方差和噪声水平的显着范围。此外，还对两个物理体模进行了实验，