Computer vision-based surface defect identification method for weld images

Welding is the main connection type used in steel structure bridges. As the weld quality significantly affects the performance of a structure, weld defect detection is crucial in the performance evaluation of bridges. With the continuous development of computer vision (CV) techniques and image acquisition equipment, CV-based methods are being increasingly applied to weld defect detection. However, accurate identification of welding defects from several images is challenging. This paper proposes a CV-based method for detecting and quantifying surface porosity defects in welds. CV techniques were used to identify and extract weld defects, and the pixellevel sizes of the weld defects were obtained. The actual sizes of the weld defects were calculated based on a conversion between the image size and actual size. The results show that the proposed image processing method has low complexity and high accuracy in detecting weld defects, providing accurate measurement of the defect size. It is a fast and non-contact detection method for weld defect detection.