At present, wind turbine blade inspection suffers from high-resolution imagery, extremely small defects, and complex morphologies, making accurate identification and localization of surface flaws difficult. To address these challenges, this article proposes a small object perception-enhanced defect detection method for turbine blades. Firstly, a dynamic channel spatial convolution module is constructed to improve the YOLOv8 detection network. By using spatial and channel reconstruction modules, the computational load of the model is reduced and feature extraction redundancy is lowered, thereby enhancing the detection performance of the model. Secondly, a small object perception enhancement network is designed, which consists of a multi-scale Transformer block, a feature fusion module, and a small object detection head. The multi-scale Transformer block assists the network in understanding the semantics of the surrounding areas of small objects, including a multi-scale fusion module, a multi-layer perceptron, and a query selection module, to achieve coarse extraction of small object defect features. Subsequently, bilinear interpolation and context-guided attention fusion mechanisms are employed to align the size and semantics of shallow and deep defect features, enhancing the model′s perception of small object defects. Finally, an adaptive distribution powerful IoU Loss function is introduced to improve defect localization accuracy and reduce the impact of class imbalance on detection accuracy. Experiments implemented on a self-built offshore wind turbine blade dataset demonstrate that the proposed defect detection network achieves an average precision of 0.815. Compared with the YOLOv8 and RT-DETR models, it shows improvements of 0.134 and 0.182, respectively. Moreover, it achieves an inference speed of 14 frames per second on an RTX3090 GPU, meeting the requirements for real-time detection and further proving its potential for application in wind turbine blade defect detection.