Ineffective object recognition models occur in remote sensing images through complex background interference and dense target integration. To this end, this study improves the YOLOv5s object output model. First, a mixed attention menu is utilized to improve the convolutional attention model (CBAM) and add it to backbone networks. Accordingly, the extracted features of the model contain local and global information to enhance the model’s ability to identify targets in complex backgrounds. Then the study uses the ultra-light sampler DySample to reduce model parameters and improve model performance. Finally, the study employs the EIoU loss function to improve the positioning level of the target to be detected. Experimental verification of RSOD and DIOR data sets shows that the improved YOLOv5s has a 7.8% higher accuracy than the original model in detecting targets in remote sensing images, meeting the real-time detection requirements of targets in remote sensing images. In addition, the improved model retains the advantages it has in comparison to other object recognition models.