To achieve higher-precision detection of metal surface defects, a defect measurement method based on binocular vision and pyramid SGBM(semi-global block matching)was proposed. First, image enhancement was performed using contrast limited adaptive histogram equalization (CLAHE), bilateral filtering, and guided filtering to remove redundant noise from images and improve their clarity. Second, hierarchical optimization was implemented using image pyramids and ORB (oriented FAST and rotated BRIEF) feature detection to enhance the computational efficiency and accuracy of the algorithm. Finally, through disparity post-processing, a more accurate disparity map was obtained using weighted least squares (WLS) filtering and subpixel refinement with Gaussian weights. Experimental results show that the proposed method achieves significant effects in metal surface defect measurement: the reconstructed 3D point cloud has a clear structure, the accuracy of the improved pyramid SGBM algorithm is significantly enhanced, and the relative error of metal surface defect size measurement is less than 5%, which meets the requirements of practical detection. The proposed method can effectively eliminate the interference of metal surface reflection and complex noise on stereo matching, while balancing computational efficiency and measurement robustness. It can provide technical reference and practical solutions for 3D reconstruction and quantitative dimension detection of surface defects of metal components in industrial scenarios.