Oriented to object detection in optical remote sensing images, this study proposes an improved Single Shot multibox Detector (SSD) model aiming at typical objects, i.e., aircraft and car, in the images. First, a multi-scale feature fusion module is introduced to the SSD network model to fuse deep features and shallow features. As a result, more contextual information of features can be obtained and the network’s ability to extract object features is enhanced. Then, cluster analysis is performed according to the size distribution characteristics of target samples in the data set to obtain more accurate default bounding box parameters, thereby effectively improving the network’s ability to extract target location information. Finally, the proposed model is compared with SSD and YOLOv3 models on data sets common for object detection in remote sensing images, which demonstrates that the mean Average Precision (mAP) of object detection has been greatly improved and verifies the effectiveness of our model.