Aiming at the problems of low detection accuracy, large model parameters, and poor real-time performance of existing algorithms in surface defect detection of rolling bearings, an improved YOLOv10n rolling bearing surface defect detection algorithm is proposed. On the backbone network, redesign C2f using GhostConv, MSMHSA module, and CGLU module, construct CGMC2f module to enhance the model′s feature extraction capability and reduce the model′s parameter count; in SPPF, the SPPF-LSKA module is designed by combining GroupConv, Residual-Conv, and Fusion-Conv modules to construct a new GRFSPPF-LSKA module, effectively solving the problem of information loss and improving the model′s multi-scale feature extraction and fusion capabilities; on the Neck network, combining the multi-scale feature weighted fusion of BIFPN, MAF-YOLO network, and EMCAD module, an EMBS-FPN network is constructed to improve the detection accuracy of the model, reduce the number of model parameters, and make the model lightweight; drawing on the Focal-loss approach, optimize the CIoU loss function to Focaler-CIoU to accelerate the convergence speed of the model. The experimental results showed that the improved YOLOv10n achieved a mAP of 92.6%, an increase of 2.7% compared to the original model, a reduction of 0.45 M in parameter count, and a decrease of 0.6 GFLOPs in computational complexity, better meeting the real-time detection requirements of rolling bearing surface defects.