Multi Angle Rotation Object Detection for Remote Sensing Image Based on Modified Feature Pyramid Networks

Abstract

ABSTRACT Remote sensing data generated by satellites have important research and application values. However, traditional detection models have poor adaptability and generalization ability. In order to solve the low accuracy problem of the existing algorithm for small object detection of remote sensing, a small object detection algorithm MARNet (multi-angle rotation network) for remote sensing images of multi-angle rotation was proposed in this study, which used ResNet101 (residual network) as the baseline network. Global attention feature pyramid networks (GA_FPN) structure was designed based on the features of the pyramid network to improve the small object detection performance in remote sensing. Then MergeNet (Merge Network) was designed to better obtain the semantic relationship between features, and the attention mechanism was introduced to enhance the feature information of the target object. Datasets of DOTA (a large-scale dataset for object detection in aerial images) and NWPU VHR-10 (northwestern polytechnical university, very-high-resolution) are used to verify the algorithm.