Bird nest encroachment on overhead transmission lines can cause safety hazards to the power equipment on the towers, which may indirectly affect the stable operation of the whole power system. Aiming at the current overhead transmission line bird′s nest detection model in the complex scene as well as the small target scene detection accuracy is not high, the detection efficiency is low, the model is complex and other problems. This study proposes a lightweight overhead transmission line bird′s nest detection network based on YOLOv5s framework. Firstly, the YOLOv5s feature extraction network is reconstructed by Fasternet in the backbone part, which reduces the model complexity and improves the operation speed; then the ConvMixer layer is embedded in the feature fusion network part, and the structural design of the ConvMixer layer helps to better capture the relationship between space and channel in the feature information, which improves the model′s detection ability for small targets; finally, the Finally, the ODConv module is introduced in the feature fusion network part, so that the feature map sent to the detection head contains more effective features to improve the detection performance of the model for complex scenes and small targets. The experimental results show that compared with the baseline model YOLOv5s, the computational amount and model volume are reduced by 86% and 72%, the average accuracy reaches 96.4%, and the detection speed reaches 104.2 frames/s, which verifies the effectiveness and feasibility of the improved model in this paper.