Embedded System Implementation of Ultrasonic Flaw Detection Algorithm Based on Support Vector Machine Classification

Support vector machine (SVM) based classifiers can be used to predict the presence and location of flaw echoes in Ultrasonic NDE signals with high accuracy. In this work, we present the implementation of the SVM based flaw detection algorithm on an embedded hardware platform (Tegra TK1 board) based on ARM CPU cores and a graphics processing unit (GPU). This implementation exploits high level of parallelism inherent in the algorithm and aims to achieve real time operation. Performance evaluation is done by comparing the embedded GPU against a PC setup with an Intel CPU and a discrete GPU unit. Overall, classification algorithm can be executed under 2ms with this embedded platform, potentially enabling real-time ultrasonic NDE applications.