In order to solve the problem that the component defect detection in the transmission line inspection image is easily disturbed by the background environment and the defect target scale is different, resulting in low detection accuracy, an improved YOLOv10n transmission line component defect detection algorithm was proposed. Firstly, the RepViTBlock and ELA attention mechanism were used to redesign the C2f, and the ERC2f module was constructed to suppress the background environment interference, enhance the feature extraction ability of the model, and reduce the parameter redundancy. Secondly, the DASF neck structure was designed by combining dynamic upsampling DySample and attention scale sequence fusion module ASF to improve the multi-scale feature fusion ability of the model. Thirdly, based on the DBB of diversified branch blocks, a reparametric shared convolutional detection header RSCD is proposed, which reduces the redundancy of the header parameters and strengthens the interaction ability of feature information by sharing parameters. Finally, the MPDIoU loss function is optimized to Inner-Wise-MPDIoU by drawing on the ideas of Inner-IoU and WIoUv3 to accelerate the model convergence process and improve the defect positioning accuracy. The experimental results show that the accuracy of the improved algorithm for the detection of transmission line component defects reaches 92.1%, which is 3.4% higher than that of the original algorithm, and the number of parameters and GFLOPs are reduced by 19.4% and 0.4, respectively, which proves the effectiveness of the improved algorithm.