As a core component of clean energy systems, wind turbines are prone to various surface defects that severely impact operational efficiency and safety, making timely detection and treatment crucial. To address issues such as missed detections, false alarms, and insufficient accuracy in small target detection, this paper proposes an improved YOLOv8-based algorithm for wind turbine surface defect detection: PSSN-YOLO. The algorithm introduces a small target detection layer to provide multi-scale information, employs the Slim-neck paradigm as the feature fusion network to enhance detection accuracy while reducing model parameters, embeds the SE attention mechanism before each detection head to focus on critical feature channels and improve defect detection in complex environments, and optimizes the loss function using normalized NWD distance to better measure bounding box similarity. Experimental results demonstrate that the improved algorithm achieves increases of 1.1%, 4.4%, and 2.6% in precision (P), recall (R), and mAP50, respectively, while reducing parameter count by 8.97%, better satisfying the practical requirements for wind turbine surface defect detection.