As an important part of a bearing, a bearing ring (BR) is prone to producing various defects on each surface in the production process, which seriously affects the reliability of the bearing. To solve the problem in which multiple defects are randomly distributed on multiple surfaces and manual detection is difficult, an automatic method for detecting defects on the whole surface of BRs based on machine vision is proposed. Firstly, the characteristics of the BR's surface defects are analyzed, and an efficient scheme for acquiring the whole surface image of the BR is designed. Then, the method for detecting the defects on the whole surface of BRs is developed, and the corresponding image preprocessing, region of interest (ROI) extraction and defect recognition algorithms are designed. Finally, a visual inspection system to identify the defects on the whole surface of BRs based on a multi-station turnover process is developed. On the premise of determining the key parameters of the detection algorithm, the performance of the detection method is analyzed through the experimental method. The results show that the comprehensive accuracy of the proposed detection method is 95%, which meets the detection requirements. On this basis, the detection strategy is optimized, and the best parameter combination is obtained through the experiments, which further reduces the false detection rate of good products and the missed detection rate of defective products, both of which are less than 3.5%.