Welding Defects Detection in Radiographic Images Using an Improved Denoising Technique Combined with an Enhanced Chan-Vese Model

The detection of welding defects is becoming an important operation in the industry and the field of non-destructive testing. Among the most used techniques in the detection of weld defects, it is radiography. The radiographic images acquired are generally of low contrast, poor quality, and uneven lighting. Therefore, the detection of welding defects becomes a difficult task. In this work, a new hybrid approach based on the combination of several techniques is proposed. It consists of three stages: firstly, we define the region of interest (ROI). Secondly, a preprocessing operation based on an improved version of denoising by soft thresholding of wavelet coefficients and an optimized threshold is applied to improve the image quality (noise reduction, contrast enhancement). Thirdly, an enhanced Chan-Vese model is proposed to segment the denoised ROI region. This enhanced model is based on the choice of a cluster obtained by the Fuzzy C-Mean algorithm (FCM) as the initial contour. The proposed approach is applied to the various radiographic welding images from the GDxray database to extract the characteristics of the welding defects. The results obtained clearly show the effectiveness of the proposed approach compared to conventional techniques.