Aiming at the problems of the current fan blade defect detection algorithm, such as insufficient detection accuracy, high incidence of false detection under complex background and inconvenient deployment of model, an improved YOLOv8n fan blade defect detection algorithm was proposed. Firstly, a new Extra-IB module and C2f-Extra-IB module are introduced to improve the key modules in MobilenetV2, which are used to reduce the number of model parameters to achieve lightweight and pass high-quality feature maps for subsequent feature fusion. Secondly, the AEMFP module is proposed to replace the SPPF module, which innovatively integrates the EMA attention mechanism and parallel substructure design to improve the multi-scale feature fusion and feature adaptive extraction capability of the algorithm. Finally, ELA attention mechanism is introduced into the neck network to reduce the influence of complex environment on the detection effect and improve the detection accuracy of small targets. Ablation experiments and comparison experiments were conducted using fan blade surface defect data set. The proposed algorithm mAP reached 81.7%, an increase of 5.1% compared with YOLOv8n. The number of model parameters and floating-point calculations were 2.09×106 and 5.4 GFLOPS, respectively, decreasing by 22.3% and 21.7%. The model size is reduced by 19.8% and the detection frame speed reaches 45.57 frames.It shows that the improvement measures proposed in this paper can not only improve the detection accuracy of the algorithm, but also achieve lightweight, which can meet the demand of using the detection equipment with limited computing resources such as UAV for efficient and accurate fan blade defect detection.