To address the issue of low detection accuracy in remote sensing image target detection methods for small devices with limited resources, an efficient and lightweight method based on the improved YOLOv7-tiny algorithm is proposed. To address the issue of dense distribution of small targets in remote sensing images, a low-span context decoupling detection head module is designed. This module fuses deep and shallow features to perform classification and regression tasks for target detection. It effectively solves the problems of leakage and misdetection of small targets in remote sensing images. Meanwhile, a parallel series attention mechanism is proposed to enhance the network’s ability to extract multi-scale target features for remote sensing image targets. This is achieved by combining the parallel three-branch network with the spatial attention module. Additionally, the model’s generalization ability is improved through the introduction of the Focal-EIoU loss function. The comparison experiments, ablation experiments, deployment experiments and generalization experiments were conducted on the model. Experimental results indicate that the detection accuracy on DIOR-5s and NWPU VHR-10 datasets improved by 2.6% and 1.7%, respectively, compared to the original model. The model size is only 19.1 MB, and the detection speed is 64.1 fps, verifying the algorithm’s effectiveness.