Objective To address the deployment challenge of traditional object detection models in low-computation scenarios e. g. drones and satellites this study proposes a lightweight remote sensing image object detection algorithm based on DETR Detection Transformer which reduces model complexity while preserving detection accuracy. Methods Firstly the proposed model employed an EfficientViT feature extraction module as a lightweight backbone for image feature extraction and selection. Then a lightweight and efficient hybrid encoder was designed to reduce the number of parameters and computational cost of the model while maintaining detection accuracy. This encoder comprised two key components the S-AIFI Slim-Attention-based Intrascale Feature Interaction module which focused on processing deep features to enhance contextual aggregation of feature information and the MSFM Multi-Scale Feature Fusion Module which improved detection capability for objects of varying sizes in remote sensing images through effective multi-scale fusion. Furthermore a shape-IoU loss function was incorporated to refine the detection precision of the model. Results Experiments on the DOTA-v1 and SIMD datasets showed that the model achieved mean average precision mAP scores of 75. 5% and 81. 9% respectively with its parameter count reduced to 10. 3 M. Conclusion The trained model exhibits a small memory footprint and low parameter count making it suitable for remote sensing image processing applications with limited computational resources.