Automated detection of welding defects without segmentation

Abstract

Substantial research has been performed on automated detection and classification of welding defects in continuous welds using X-ray imaging. Typically, the detection follows a pattern recognition schema (segmentation, feature extraction and classification). In computer vision community, however, many object detection and classification problems, like face and human detection, have been recently solved -without segmentation- using sliding-windows and novel features like local binary patterns extracted from saliency maps. For this reason, we propose in this paper the use of sliding-windows with the mentioned features to perform automatically the automated detection of welding defects. In the experiments, we analyzed 5000 detection windows (24×24 pixels) and 572 intensity features from 10 representative X-ray images. Cross validation yielded a detection performance of 94% using a support vector machine classifier with only 14 selected features. The method was implemented and tested on real X-ray images showing high effectiveness. We believe that the proposed approach opens new possibilities in the field of automated detection of welding defects.