Target detection in remote sensing image based on deep learning

For high-resolution optical remote sensing images, there are still many challenges in target detection. In this paper, deep learning algorithm is used to detect the target in remote sensing image. Improve and optimize the deep learning target detection algorithm. When the selected data set is used for target detection, the AP value is improved, which leads to the concept of multi-scale feature fusion feature pyramid and residual network. By improving the selected Yolov3 network model, the detection effect of the two targets of aircraft and ships in remote sensing images has been significantly improved.