To address the challenges of low detection accuracy for small-scale defects and excessive model parameters in wind turbine blade surface inspection, this paper proposes an improved detection framework, CESL-YOLO, based on YOLOv8n. First, the convolutionalblockattention module（CBAM） is integrated into the backbone to enhance the model′s focus on defect regions during feature extraction. Second, the neck structure is redesigned as an EfficientFPN, where re-parameterized convolution is employed to improve multi-scale feature fusion efficiency. Third, the SIoU bounding box regression loss function is adopted to accelerate model convergence. Finally, a lightweight sparse-separable branch (LSSB) detection head is developed to improve defect localization accuracy. Experimental results demonstrate that CESL-YOLO outperforms the baseline model, achieving gains of 2.7%, 4.8% and 5.3% in precision, recall and mAP, respectively. Moreover, the model size is reduced from 3.01 M to 2.82 M parameters, making it more suitable for practical wind turbine blade surface defect detection tasks.