Abstract: The depth of welding defects on the guardrail columns of municipal roads varies. Conventional infrared detection methods can cause the temperature difference signals of welding nodes with different degrees of defects to converge due to thermal diffusion effects, resulting in incorrect detection results and misjudgment of structural safety, which poses potential risks to the safe operation of municipal roads. Therefore, the depth detection of welding node defects in municipal road guardrail columns is completed through infrared thermal imaging. By using multi-scale guided filtering and local saliency map weight fusion, the base layer and detail layer of the original welding infrared thermal image are respectively enhanced to reduce the blurring effect caused by thermal diffusion, highlight the temperature gradient characteristics of the defect area, and visualize micro defects. By mapping the enhanced image to a weighted image and using a Gaussian fitting based brightness model and saliency constraint to highlight the defect area, the maximum flow/minimum cut algorithm is used to solve the image cutting problem and obtain the defect area. Therefore, based on the region growth criterion, region growth is carried out according to the average gray level of the defect area and the gray level of the current growth point. By comparing the defect depth with the thickness of the guardrail column, the true boundary of the defect is accurately captured, and the recognition of the defect depth is completed. The experimental results show that the overlap between the segmentation results obtained by the proposed method and the actual welding node defect areas is close to 99%, and the accuracy of defect depth detection on 500 red external thermal images is as high as 99%. The proposed method can accurately and efficiently complete the de