Wind turbine blades, being the core component of wind power generation systems, have their health status directly impacting the overall power generation efficiency and operational safety. Addressing the challenges of blade defect detection, researchers delved into the YOLOv8n network and innovatively proposed the Efficient Multi-Scale Convolutional module (EMSConv). This module effectively replaces the convolutional layers in traditional residual blocks, significantly reducing the interference of redundant features on detection results through grouped convolution techniques, thereby enhancing detection accuracy. Furthermore, in the detection head, a diverse set of attention mechanisms from Dynamic Head are incorporated. These self-attention mechanisms work in concert, spanning across different feature layers, to achieve precise perception of target scales, spatial locations, and detection tasks, vastly strengthening the comprehensive capabilities of the target detection module. Moreover, researchers innovatively integrated Inner-IoU, Wise-IoU, and MPDIoU, creatively proposing Inner-Wise-MPDIoU to replace the traditional CIoU loss function. This move not only improved the network’s detection precision but also accelerated the convergence process. During testing on a self-made dataset of wind turbine blade defects, YOLOv8-EDI exhibited remarkable performance, achieving an mAP50 value of 81.0%, a 2.3% increase compared to the original YOLOv8n. The recall rate also reached 76.8%, marking a 3.7% improvement. While enhancing detection performance, this model managed to reduce computational requirements by 5.5%, fully meeting the need for efficient, accurate, and large-scale blade defect detection in industrial settings.