Bird’s nests on transmission line pylons can pose a threat to the safe operation of power equipment and even affect the stability of the entire power system. To address the problem of poor applicability of existing methods for transmission line bird nest detection, this paper proposes an attention-guided feature fusion-based transmission line bird’s nest detection method (AGFF-YOLOv5). Based on the YOLOv5 object detection model, the method designs feature balance network by combining channel attention mechanism and spatial attention mechanism, and uses channel weights and spatial weights as guides to achieve the balance of semantic and spatial information between features at different levels of the detection network. Meanwhile, in order to avoid losing feature information due to the increase of network layers, a feature enhancement module is proposed to capture the channel information and location information related to the target. Finally, the bird"s nest dataset is built and compared with the transmission line UAV inspection images for experiments. The experimental results show that the AGFF-YOLOv5 transmission line bird nest detection method has strong generalization capability and applicability, and also provides technical references for other power fault detection.