Addressing the issues of significant scale differences in defect targets, strong background interference and potential missed detection of small and occluded targets in aerial images of transmission lines, a poly feature fusion detector based on YOLOv11n ( PFD-YOLO) is proposed. Firstly, by incorporating poly kernel Inception block （PKI Block） from poly kernel Inception network （PKINet） to enhance C3K2, the model complexity is effectively reduced while the feature extraction capability is enhanced. Secondly, a feature focusing diffusion pyramid network (FFDPN) is designed to reconstruct the feature fusion structure on the Neck side. The feature focusing module (FF) facilitates efficient interaction and contextual information propagation among features of different scales, alleviating the feature forgetting problem caused by inconsistent multi-scale features. Thirdly, a lightweight shared convolution detection head (LSCD) is introduced, utilizing cross-scale parameter sharing and group normalization strategies to significantly reduce the computational overhead of the detection head while enhancing the detection accuracy of small targets. Finally, the proposed method is validated on a self-built multi-target defect dataset of transmission lines. Experimental results show that PFD-YOLO maintains a high inference speed while improving mAP50 and mAP50.95 to 86.6% and 53.9%, respectively, representing an increase of 3.9% and 3.3% compared to the original YOLOv11n model. This effectively enhances the detection accuracy and robustness of transmission line defects and foreign objects, demonstrating good engineering application value.