To address low detection precision of remote sensing image object detection caused by complex backgrounds， numerous small objects， and long-tail objects， a remote sensing image object detection algorithm named SLT-DETR （Small and Long-Tailed object-aware real-time DEtection TRansformer） was developed on the basis of improved Real-Time DEtection TRansformer （RT-DETR）. First， a Multi-scale Feature Enhancement Pyramid （MFEP） was employed to optimize the original continuous convolutional fusion， and an OmniKernel module was introduced to further enhance the ability of small object detection. Then， the content-guided attention was utilized to fuse features output by P3， P4， and P5， thereby the addressing information distribution imbalance effectively. Finally， a FasterBlock module was adopted for lightweighting model with improved inference speed. Experimental results show that on the self-constructed remote sensing dataset， SLT-DETR improves the mean Average Precision （mAP） by 5.0% compared to RT-DETR， with the number of model parameters reduced by 1.9×10⁶； compared with classic models YOLOv8m， YOLOv8l， and Swin Transformer， SLT-DETR achieves mAP improvements of 5.7%， 3.7%， and 10.9%， respectively； on the RSOD and HIT-UAV datasets， SLT-DETR achieves improvements in mAP of 1.1% and 1.3%， respectively. It can be seen that the proposed model enhances the detection of small and long-tailed objects effectively while maintaining lightweight model design.