The work aims to propose an adaptive image clustering method for overlapping defects on work-piece surface, so as to solve the problem that the complex and mutually interfered defects on work-piece surface are difficult to be separated automatically and classified and identified by images. Firstly, the binary image of the workpiece surface defects was extracted. The principal component analyzer of mixed probabilities was used to estimate the local tangent space of each pixel on the defects and improve the similarity matrix between the local tangent space of each defect location. Then, the clustering center point and quantity were determined through the improved density peaks adaptive method based on the similarity matrix. Finally, the pixels included in each analyzer were assigned to the different defect manifolds through SMMC to realize the separation and detection of workpiece defects. Firstly, the good and accurate separation effect of this method on the interfered structure defects was verified by comparing the calculated and actual length and width. The average relative errors were 0.957% and 0.650%. Secondly, in order to reflect the effectiveness and superiority, this method was compared with k-means clustering and spectral clustering, which proved a good clustering effect. Finally, the stability of the method was tested. The average ME value was below 6% for the statistical test results, and the correct cluster number rate was as high as 99%~100%. The experimental results show that this method can automatically separate different defects that interfere with each other in the surface image of the workpiece more accurately.