In order to solve the problem of low detection precision caused by complex background environment, large target scale difference, various categories and uneven distribution, an improved YOLOv11n defect detection algorithm for fan blades is proposed. Firstly, an efficient multi-scale convolution EMSConv was innovatively proposed, and C3k2 was redesigned to enable the model to efficiently capture the input feature map information and enhance the detection accuracy; secondly, introduce the large separable kernel attention mechanism and Residual-Conv design SPPF_LSKR module to replace the original pyramid pooling module, enrich the context information and improve the multi-scale feature extraction and fusion capabilities of the model; thirdly, the re-parameter shared convolution detection head (RSCD) is adopted to reduce the number of parameters and computational load of the head by sharing parameters, thereby enhancing the speed and accuracy of the defect detection task; finally, a loss function Inner-Wise-MPDIoU is proposed based on the ideas of MPDIoU, Inner _IoU and Wise_IoU, which balances the detection of defects of different scales and accelerate the convergence speed of the model. The results of the experiments indicate that the modified YOLOv11n model attains an mAP value of 89.2%, which is 2.9% higher than that of the original model. The results show that the improved model can meet the needs of efficient and accurate detection of fan blade defects.