Classification and Contour Recognition of Welding Defects in Magneto-Optical Images

In the field of magneto-optical imaging nondestructive testing for welding defects, multi-angle detection of welding defects has already been achieved. However, research on automatic defect recognition and contour extraction remains insufficient. Therefore, to enable automatic detection of welding defects using magneto-optical imaging technology, it is essential to address the key issues of defect recognition and contour extraction in magneto-optical images. The dataset in this article includes five types of images: defect-free, lack-of-fusion, cracks, pits, and Weld reinforcement. Firstly, the Mask R-CNN detection method is used to perform defect recognition and contour segmentation on the original magneto-optical image dataset. The detection results indicate that the recognition rate of lack-of-fusion and Weld reinforcement in the original magneto-optical image is not high, and the recognition accuracy of pits and cracks is extremely low. Subsequently, the magneto-optical image dataset was preprocessed using the differential level set method, and the mask R-CNN algorithm was used to identify defect types and segment defect contours. Comparing the results of two experiments, it was found that the detection accuracy of the preprocessed dataset was higher, and the overall recognition accuracy increased by 30%.