Object Detection in Remote Sensing Images Based on Improved SSD Algorithm

In the field of object detection in remote sensing images, most of the existing object detection algorithms perform poorly for small objects.This paper proposes an algorithm that fuses multi-scale features for object detection in remote sensing images.The features are first extracted by using the basic network of the SSD algorithm to form a feature map pyramid.Then the feature map fusion module is designed to fuse the position information of the shallow feature map and the semantic information of the deep feature map, retaining rich context information.Finally, a module to remove redundant information is designed, and the features in the feature map are further extracted through the convolution operation.The feature information is also screened to reduce the aliasing effect brought by the fusion of the feature maps.The experimental results on NWPU VHR-10, a dataset of remote sensing images, show that the proposed algorithm achieves an average detection accuracy of 93.9%, demonstrating that it outperforms Faster R-CNN, SSD and other algorithms in detection of small objects in remote sensing images.