In order to solve the problems of dense arrangement of small targets and complex background area in remote sensing image object detection, the YOLOv5s model is improved. Backbone network adopts coordinated attention (CA) module with deep separable convolution, introduces the multi-dimensional attention mechanism of channel and space, mines the correlation between spatial direction and position, and improves the ability of feature extraction and long-distance dependency capture. Neck network uses bidirectional feature pyramid network (BiFPN) structure to fully integrate the deep and shallow feature information to improve the feature fusion effect at different scales. Experimental results show that, for the remote sensing target dataset DIOR, compared with results of modle before improved, mean average precision (mAP) of the model is increased by 9.8 percentage points after the improvement. Average precision (AP) of all categories has been improved, and the value of most categories has increased by more than 5 percentage points. The precision is increased by 7.2 percentage points, the recall is increased by 10.8 percentage points, which alleviates the problems of missed detection and false detection, and enhances the detection effect of the model on dense small targets in complex backgrounds in remote sensing images.