Aiming at the problem of low accuracy in existing bird nest detection on transmission towers, a UAV (Unmanned Aerial Vehicle)-based bird nest detection system for power transmission lines based on the Lightweight Residual Convolutional Attention Network (LRCAN) is proposed. On the basis of analyzing the workflow, a bird nest detection model for power transmission lines based on LRCAN is proposed, which enables the network to focus more on the required detail features and suppress the interference of other irrelevant information. The normal convolution in the feature fusion network is modified by using depthwise separable convolution layers to reduce the number of network parameters. Simulation results show that compared with YOLOX-S, which has a similar number of parameters, the proposed model has increased the mAP (mean Average Precision) by 5.4%. Compared with YOLOX-L and YOLOX-X, which have the same level of mAP, the number of parameters in the proposed model is reduced to 1/5 and 1/10 of theirs, respectively.