Optical Remote Sensing Image Target Detection Based on Improved Feature Pyramid

At present, many deep-convolution-based remote sensing image target detection methods have been developed and have achieved higher detection accuracy and faster detection rate. However, they do not perform well in the face of datasets with large target scale changes and multiclass and dense small targets. Therefore, solving the problem of scale change of remote sensing images is the focus of our research. An improved feature pyramid model named feature enhancement feature pyramid network (FE-FPN) is presented in this article. The FE-FPN utilizes a channel enhancement module (CEM), unpooling feature fusion (UPFF), and adaptive pooling spatial attention module (APSAM) to reduce information loss during the generation of feature maps and improve its capability to represent feature pyramids. The CEM is designed to expand the receptive field and learn important features adaptively, the UPFF is designed to improve the feature fusion method to avoid feature conflicts, and the APSAM is used to complement high-level feature information. The average precision of our models using ResNet50 is 2.0% higher when the FE-FPN is replaced by the feature pyramid network in Cascade R-CNN. The proposed FE-FPN model is quantitatively compared with several classical characteristic pyramid models, which proves that the performance of the FE-FPN is superior to that of other models.