Aiming at the problem of defects occurring on the surface of 3D metal printed parts due to technology and materials, a surface defect detection method for 3D metal printed parts based on an improved Canny algorithm is proposed, which processes the defect images of 3D metal printed parts by using the improved algorithm. First, an improved bilateral filter is used to replace Gaussian filtering to handle the surface defect images of metal printed parts under salt- and-pepper noise. Then, a dynamically weighted four-direction Scharr operator is adopted to calculate the gradient amplitude and determine the gradient direction, so as to better highlight the edge information of 3D metal printed parts. Next, an improved non-maximum suppression algorithm is employed to further process the image. Finally, the dynamic threshold and hysteresis edge tracking algorithm are utilized to realize the selection of high and low thresholds and enhance the continuity of edges. By comparing the defect detection results of different algorithms on the surface of 3D metal printed parts, the experimental results show that the improved Canny algorithm performs better in noise smoothing and edge continuity than the traditional algorithm. Specifically, the peak signal-to-noise ratio (PSNR) value is increased by 46.70% compared with the traditional Gaussian filter, the structural similarity index (SSIM) value is improved by 39-93%, and the probability of figure of merit (PFOM) value of the processed image is enhanced by 36.46% compared with that of the traditional Canny algorithm. This method can effectively detect the defects on the surface of 3D metal printed parts and has strong practicability.