Whether the quality of parts is qualified or not affects the service life of the entire assembly. How to quickly and accurately detect whether the quality of parts is qualified has become one of the research hotpots. Machine vision defect detection is increasingly used, but due to the existence of the texture background of the parts after milling, It often leads to insufficient precision in the detection of surface defects of parts. This paper proposes a new image surface defect extraction method that combines the gray-level co-occurrence matrix and the fuzzy C-means clustering algorithm. The improved gray-level co-occurrence matrix is used to increase the contrast between the defect and the milling background, and then the defect and the milling background are used to increase the contrast. For the feature of large gray scale difference between milling backgrounds, fuzzy C-means clustering method is used to segment the image. The algorithm can effectively distinguish processing defects and processing textures, and quickly and accurately extract part defect features. Through the experiment of defect extraction, and compared with the traditional segmentation algorithm, it can be concluded that the algorithm can quickly extract the surface defects of milling parts, and has good adaptability to extract multiple types of defects. Keywords: Milled parts; machine vision; Surface defects; feature extraction; intelligent algorithm.