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In the process of structural smoothness detection of multi workpiece splicing weld surface, due to the complexity of the splicing surface and the noise in the image, the details are not obvious, which reduces the detection accuracy and efficiency. For this reason, a visual inspection technology for the structural smoothness of multi workpiece splicing weld surface is proposed. Using dictionary learning methods to eliminate noise in weld surface images; Input it into the MRFENet network, extract image features, and achieve enhanced processing of weld surface images; Using incremental two-dimensional principal component analysis to extract the smoothness features of the weld surface structure and achieve smoothness detection. The experimental results show that the proposed method has good image processing performance, high detection accuracy, and high detection efficiency.