In order to ensure the speed and accuracy rate of location, the YOLO v2 network with the best detection effect in the field of object detection in 2016 is used to make the target detection data sets with the obvious features of surface features as the object area. Through the dimension clustering of object box, classified network pre-training, multi-scale detection training, change the candidate box filtering rules and other methods, the YOLO v2 network is improved, and it can better adapt to the location task. The network is able to locate the object area in the aerial image acquired from the unmanned aerial vehicle in real time. And the latitude and longitude of unmanned aerial vehicle are obtained by the projection relationship and coordinate transformation. The experimental results show that the proposed method can achieve better effect, and the average accuracy rate of the detection network increases to 79.5% in the object area detection task of the aerial image. It is verified by simulation experiment of simulated flight, the accuracy rate of the network location is over 84% in the aerial image that contains the object area.