To improve the intelligent design of the rural power distribution network， this paper proposes to identify the typical power targets that affect the design of the distribution network in the aerial images using deep neural networks. Firstly， we use UAV to obtain high spatial resolution aerial images of the distribution network planning area， and construct a data set containing 11 categories and 32 118 typical power targets. Then， through the practical comparison of Faster-RCNN， YOLO and single shot multibox detector （SSD） methods， SSD is selected to detect and identify typical power targets. Finally， feasible areas of distribution network pole planning are obtained. Experimental results show that compared with Faster-RCNN and YOLO， SSD can effectively detect and identify typical power targets such as the substation， distribution room and box transformer， and the recognition accuracy reaches 68.5%， which meets the practical requirements. The proposed method provides the technical support for the power design， reduces the labor cost and improves the efficiency of distribution network design.