The maintenance of power system is an important guarantee for the stable operation of power system. The power inspection based on intelligent algorithm provides convenience for the maintenance of power system. Extracting power line is a key technology for autonomous power inspection and ensures the safety of aircraft at low altitudes. At the same time， extracting power line using deep learning is an important breakthrough of power inspection. Based on these， deep learning is applied to extract power line. Combined with the characteristics of power line， the improved strategy of inputting image and attention module are embedded. The model of extracting power line based on stage attention mechanism （SA-Unet） is proposed. In the coding stage， the stage input fusion strategy （SIFS） is adopted to make full use of the multi-scale information of the image to reduce the loss of spatial information. In the decoding stage， the features of power line are focused by the embedded stage attention module （SAM）， and high-value information is screened out from a large amount of information quickly. Experimental results show that the method has good performance in multiple scenes with complex backgrounds.