Detection and Recognition of Defects in X-ray Images of Welding Seams under Compressed Sensing

Abstract

In order to solve the defects in welding seams of submerged arc welding, the X-ray images were used to detect defects. By comprehensive analysis and experimental study, the offline database of defects in welding seams was established, and online intensification and segmentation algorithm as well as recognition method based on the compressed sensing for defect images were designed. First, the defect database of X-ray images of welding seams was established by the offline data. After the welding images were acquired, the defect segmentation and acquisition algorithm based on the clustering method were proposed. A series of characteristic values of defects in the offline database were used as atoms in the compressed sensing algorithm dictionary, and atoms were optimized with the PCA method, to facilitate the improvement of the processing speed. Supported by the optimal dictionary, the category of defects was obtained. The actual analysis of circular and linear defects was carried out to give ROC curves classified in two cases.