Automatic detection and classification of weld flaws in TOFD data using wavelet transform and support vector machines

Ultrasonic time-of-flight diffraction (TOFD) is known as a reliable non-destructive testing technique for the inspection of welds in steel structures, providing accurate positioning and sizing of flaws. The automation of data processing in TOFD is required towards building a comprehensive computer-aided TOFD inspection and interpretation tool. A number of signal and image processing tools have been specifically developed for use with TOFD data. These tools have been adapted to function autonomously, without the need for continuous intervention through automatic configuration of the critical parameters according to the nature of the data and the acquisition settings. This paper presents several multi-resolution approaches employing the wavelet transform and texture analysis for de-noising and enhancing the quality of data to help in the automatic detection and classification of defects. The automatic classification is implemented using a support vector machines classifier, which is considered faster and more accurate than artificial neural networks. The results achieved so far have been promising in terms of accuracy, consistency and reliability.