Automatic weld defect identification from radiographic images

This paper presents a new approach for weld defect identification from radiographic images. This approach is based on the generation of a database of defect features using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients extracted from the Power Density Spectra (PDSs) of the weld segmented areas after performing pre-processing and segmentation stages. Artificial Neural Networks (ANNs) are used for the feature matching process in order to automatically identify defects in radiographic images. The performance of the proposed approach is evaluated using 150 radiographic images in the presence of various types of noise and blurring. The experimental results show that the proposed approach can be used in a reliable way for automatic weld defect identification from radiographic images in noisy environments, and can achieve high recognition rates.

► New approach for weld defect identification from radiographic images. ► Features are extracted from PDS of signals representing defects. ► MFCCs and polynomial coefficients are calculated for PDS signals. ► ANNs are used for feature matching process for automatic defects identification. ► Proposed approach achieved the highest recognition rates in noisy environment.