Aiming at the problems such as small object size， arbitrary object direction and complex background of remote sensing images， on the basis of YOLOv5 （You Only Look Once version 5） algorithm， an algorithm involved with geometric adaptation and global perception was proposed. Firstly， deformable convolutions and adaptive spatial attention modules were stacked alternately in series through dense connections. As a result， a Dense Context-Aware Module （DenseCAM） which can model local geometric features was constructed on the basis of taking full advantage of different levels of semantic and location information. Secondly， by introducing Transformer in the end of the backbone network， the global perception ability of the model was enhanced at a low cost and the relationships between objects and scenario content were modeled. On UCAS-AOD and RSOD datasets， compared with YOLOv5s6 algorithm， the proposed algorithm has the mean Average Precision （mAP） increased by 1.8 percentage points and 1.5 percentage points， respectively. Experimental results show that the proposed algorithm can effectively improve the precision of object detection in remote sensing images.