Welding Defects Classification Based on Adaptive SVM Decision Tree

An adaptive SVM(Support Vector Machines) based on binary tree using the degree of separation is proposed in this paper, aiming at the problem that it’s difficult for traditional detection methods to accurately identify the welding defects of X-Ray images. Firstly, mathematical morphological reconstruction is applied to the filtered X-Ray images of welding defects. It is proposed to separate category of defects with the largest degree of separation as a priority, and to construct adaptive SVM classifiers based on binary tree, thus decreasing the accumulated error. Finally, a SVM decision tree of good classification performance can be obtained, which is used to classify and identify the X-Ray images of welding defects, and it shows that the algorithm has made a good classification and recognition accuracy results.