In this paper, a method of gluing quality detection for curved workpiece based on image and point cloud is proposed, which solves the problem of low accuracy and poor robustness of image-based gluing quality detection. The method includes using the circular Mark point for rough positioning, introducing the improved iterative closest point algorithm with normal vector consistency constraint to complete the fine positioning, and extracting the glue skeleton information from the image into 3D glue trace points. The sampling points were obtained by equidistant and ordered sampling method to detect the quality parameters of the glue line. According to the normal constraint of sampling point and tangential constraint of glue trace, the sampling glue trace cross section model was obtained, and the point cloud was mapped to the cross section to get the glue trace cross section profile model. Experimental results have shown that the measured width error of the glue line is less than 0.35mm, and the thickness error is less than 0.25mm, which meets the quality evaluation requirements of glue lines in industrial scenarios.