Drone inspection is one of the important methods for power transmission line detection. To address the issues of large target scale variations, difficulty in detecting small targets, and the inability to effectively capture defect details in complex scenarios with existing power transmission line detection algorithms, an improved YOLO11 model, HCDNet-YOLO11, is proposed. Firstly, the HCDNet network is designed to replace the original feature pyramid network, reducing the model′s parameter count and enhancing its expression of features at different scales. Secondly, the MulCAA attention module is constructed, which extracts key information through a dual-branch structure of average pooling and max pooling, reducing information loss and weakening the interference of complex backgrounds on detection targets through long-range pixel dependencies. Finally, the RepConv reparameterization convolution is introduced to implement the Rep_C3k2 module, enabling the model to introduce a larger receptive field during the training phase, enhancing its nonlinear feature modeling ability. Experimental results show that the HCDNet-YOLO11 algorithm has increased the accuracy by 1.9%, recall by 6.5%, and mAP50 by 5.7% on the self-built power transmission line dataset, with a 24.42% reduction in parameters. The algorithm attains good performance on the premise of reducing the number of parameters. On the public VisDrone2019 dataset, the HCDNet-YOLO11 algorithm has increased mAP50 by 6.5% and 4.6% on the val set and test set, respectively, verifying its strong generalization ability in complex aerial scenarios.