Detecting Object in Remote Sensing Image Based on Improved RT-DETR

In order to tackle the challenges associated with low detection performance and high computational resource consumption, which result from diverse scenes, complex backgrounds, small and dense targets, and large data volumes in remote sensing images, we propose an improvement on real-time detection transformer (RT-DETR) for detecting objects in remote sensing images. By introducing the Mosaic9 data augmentation technique, we enhance the model’s adaptability to multi-scene targets, as well as its target recognition performance in different backgrounds. Furthermore, to decrease resource usage without compromising detection precision, we replace the Basic Block structure in the backbone of RT-DETR with the more lightweight FasterNet Block. Finally, we enhance the adaptive intra-scale feature interaction (AIFI) by replacing multi-head self-attention (MHSA) with deformable attention, enabling the model to dynamically adjust its attention range and better capture distinctive target features in complex scenarios. Experimental validation conducted on the DSTD dataset reveals that the modified RT-DETR model achieves a detection accuracy of 94.9%, representing an improvement of 1% compared to the baseline RT-DETR. Simultaneously, the improvements reduce GFLOPs, total parameters, and overall model size by approximately 12.4%, 15.5%, and 15.2%, respectively, thus realizing a balance between performance and lightweight architecture. Moreover, generalization experiments on the NWPU VHR-10 dataset further substantiate the enhanced model’s robustness and adaptability across diverse remote sensing scenes, confirming the efficacy and practical value of the proposed enhancements.