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Aiming at the problems of large number of parameters and high computational complexity of deep learning network in current power line detection; Based on YOLOv5, a real-time detection algorithm for power lines and towers is proposed. The network structure of feature extraction layer is simplified by reducing the number of Bottleneck, and the depth-separable convolution technique is used to reduce the computational amount of model. The mechanism of power line target box screening was analyzed, and the (non-maximum Suppression) NMS algorithmwas improved to improve the model target detection accuracy. The experimental results show that the improvement of Bottleneck can effectively reduce the number of parameters of models when the recognition accuracy increases. The model detection accuracy and recall rate reach 94% and 95%, respectively, and the volume is compressed by 20.7%. The detection speed on Jetson Nano embedded platform reaches 17.2 FPS. The detection of two kinds of power line targets achieves high recognition rate and real-time performance, which has a good reference value for UAV power inspection and navigation.