The review of aero-engine compressor blades is an indispensable part of its development cycle. To improve the traditional manual blade review process, which is time-consuming, laborious, and highly uncertain, an engine blade profile detection system based on computer vision is designed. Firstly, to achieve the blade measurement of three-coordinate detector commonly used in industrial production, the batch image extraction for portable document format is completed. The out-of-tolerance judgment of blade image is completed by using color matching and Hough transform. Secondly, for the blade image within tolerance, color matching and morphological operators are used to enhance the blade image, which improves the ratio of valuable information. A residual network is trained to complete the task of morphological anomaly detection of the blade edges. Finally, to facilitate the labeling task on a massive image dataset, a universal image classification and labeling program is designed, and a blade quality detection program is designed to verify the effectiveness of the system for blade out-of-tolerance judgment and anomaly recognition on the blade image dataset. The experiment shows that the accuracy of the system for identifying anomalies in blades with or without out-of-tolerance reaches 100% and 92. 9% , respectively, which could satisfy actual needs of industrial production.