Aiming at the problems of limited accuracy of UAV detection of foreign objects on power transmission lines, high model computational complexity and limited computational speed, a power transmission line foreign object detection method SC-YOLO which improves YOLOv8 is proposed. This method introduces StarNet to construct C2f_Star module to realize the lightweight of Neck network, effectively reducing the number of model parameters and calculation amount, and at the same time improves the feature extraction ability of Neck by increasing the dimension of feature space; adds convolution attention fusion module after the backbone network outputs feature map to improve the backbone network′s preliminary feature extraction ability of input feature map, and enhance the overall detection effect of the model; replaces the original detection head with dynamic detection head to improve the model′s dynamic adjustment ability to different inputs and the degree of attention to key information; uses WIoU as the bounding box loss function and EMA-Slide Loss as the classification loss function to improve the model′s generalization ability and detection performance. Experimental results show that the proposed SC-YOLO has 8.02% fewer computational amount than the original model, and mAP is increased by 1.4 percentage points, reaching a detection accuracy of 95.2%. RC-YOLO reduces the computational complexity while achieving a high detection accuracy, and is highly feasible and practical.