In order to meet the demand of high efficiency and accuracy of weld appearance quality defect detection in wind turbine tower cylinder industry, the 3D machine vision technology was used to develop the weld appearance quality defect detection system based on wind turbine tower cylinder. First, the point cloud data was preprocessed by point cloud filtering, point cloud segmentation and point cloud simplification to ensure the accuracy of defect evaluation in the later stage. Secondly, the contour characteristics of 3D data were obtained by slice processing and breakpoint fitting. Thirdly, the improved recursive rough extraction algorithm was used to extract the feature points, and the defect evaluation was carried out to obtain the detection results of weld appearance defects. Finally, according to the evaluation process and standard of weld defects in the system, a typical weld sample is selected to test the weld width, weld dislocation and weld straightness. The weld detection accuracy can reach 0. 001 mm, and the speed is 3 times of the current manual detection speed. The detection results show that the system has the characteristics of high accuracy, high speed and high precision, which can replace manual detection, and has a good application prospect.