Remote Sensing Object Detection Based on Global Context Attentional Feature Fusion Pyramid Network
Remote sensing object detection usually faces challenges such as large variations in image scale,small and densely arranged targets,and high aspect ratios,which make it difficult to achieve high-precision oriented object detection.This study proposes a global context attentional feature fusion pyramid network.First,a triple attentional feature fusion module is designed,which can better fuse features with semantic and scale inconsistencies.Then,an intra-layer conditioning method is introduced to improve the module and a global context enhancement network is proposed,which refines deep features containing high-level semantic information to improve the characterization ability.On this basis,a global context attentional feature fusion pyramid network is designed with the idea of global centralized regulation to modulate shallow multi-scale features by using attention-modulated features.Experiments have been conducted on multiple public data sets,and results show that the high-precision evaluation indicators of the proposed network are better than those of the current advanced models.
万方数据
维普
