Welding defects extraction method by fusing saliency information of mid-level and underlying level images

Abstract

X-ray non-destructive testing (NDT) technology is extensively utilized in the welding industry for the detection of weld defects. This paper proposes a novel defect segmentation algorithm to address the challenges of X-ray defect detection images, including low contrast, blurred edges, significant noise, and pronounced background variations. Traditional detection methods often struggle to extract low-contrast defects from weld images, so this approach integrates both underlying and mid-level image information to enhance accuracy. The process begins with a visual saliency model that generates a rough saliency map from underlying details. Next, a Pulse Coupled Neural Network (PCNN) is used to compute the saliency map at the mid-level. Finally, these two saliency maps are combined using a pixel-minimum method to produce the final image saliency map. Experimental results show that this method is highly accurate, broadly applicable, and capable of rapid defect extraction within the welding area.

Keywords

• Defect detection
• Fusion model
• PCNN
• Image saliency