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.
维普
万方数据