Remote sensing images have the characteristics asblurred terrain capture and complex background environment,which leads to a problem of low accuracy in identifying large ground-level objects.To solve this problem,an improved network model based on YOLOv5s is proposed.The proposed model adjusts YOLOv5s network models backbone extraction network and neck multi-scale feature fusion network,and introduces Swin Transformer for obtaining more feature information about the target objects.Additionally,the model prunes the modules in the main network and adds coordinate attention mechanism to enhance feature extraction and fusion effects.The proposed model is tested on small target recognition by using remote sensing dataset,and mAP value of the improved YOLOv5s network is 0.8375,which is 0.0225 higher than that of official YOLOv5s network model.Experimental results show that the proposed model effectively improves the recognition accuracy,recall rate,and mAP value in comparison with YOLO series network and EfficientDet model,and it reduces the training time by 1/12 in comparison with the YOLOv5s model.