Object detection method based on deep learning is the most popular object detection algorithm in the field of object d etection currently.Due to limitations of hardware conditions,the size of the input image for the algorithm is limited.Aerial imageries usually h ave a large size.So the sliding windows method is usually used to extract the areas first,and then the extracted ares are detected,which greatly influence the detection speed of the algorithm.This p aper provides a object detection method that is the single shot detector (SSD) based on edges gradient clustering of objects in aerial images to solve the problem.The model architecture and its working principle we re deeply expounded in this paper.Then,through the test in a dataset of 151aerial images, the detection performance of gradient clustering SSD and sliding windows SSD were evaluated an d compared in object detection speed and precision.Experimental results show tha t the gradient clustering SSD is 0.499in frames per second (FPS),2.00in senconds per frame (SPF) and 46.93in m ean average precision (mAP).Compared with the sliding windows SSD,the FPS has respec tively improved by 64.69% (the SPF has respectively raised by 40.40%),but the mA P has respectively declined by 11.72%.Thus the effectiveness of the proposed method is verified.