Development of High-Accuracy Defect Detection Algorithm for X-Ray Welding Image Inspection under Strong Noise, Low Contrast and Few Samples

Abstract

We have developed an algorithm to accurately detect unclear defects in X-ray image inspection of thick welded parts under low contrast, and strong noise. Statistical Reach Features (SRF) and High-order Local Autocorrelation (HLAC) are used as noise-robust feature extraction methods. In order to deal with a small number of defect samples, pseudo-defect data with actual noise is used for machine learning. When the discriminator is optimized for zero missing, the over-detection is significantly reduced, and the method is ready for practical application.