In order to accurately evaluate the types and number of yarn defects, an algorithm of yarn defects detection based on spatial fuzzy C-means (FCM) clustering is proposed in this paper. First, the spatial FCM clustering algorithm is used to extract the yarn strips. Then, morphological opening operation is performed on the yarn strips to obtain accurate yarn strips, and the number of pixels between the upper and lower edges of the yarn is used to calculate the measured diameter and average diameter of the yarn. Finally, the type and number of yarn defects are determined according to the standard of yarn defects. In order to verify the validity and accuracy of the algorithm, a variety of pure cotton yarns with different linear densities are tested, and experimental results are compared with the capacitive yarn defects classifier. The results show that the algorithm is in good agreement with the result of capacitance detection, and it is cheap and not easy to be affected by environmental temperature, humidity and other factors.