As a key component of autonomous driving technology, object detection technology is crucial for vehicles to achieve autonomous navigation and decision-making functions. Existing algorithms still face difficulties in meeting both detection accuracy and detection speed simultaneously. In this regard, a real-time object detection algorithm FSSD-DETR based on RT-DETR is proposed. This algorithm introduces FADC module into the backbone to optimize the feature extraction process. A small object detection layer is introduced to improve the detection performance of small targets for distant vehicles and pedestrians. Based on the SSFF module and TFE module, the neck network has been redesigned to improve the accuracy of detection. The DySample upsampling operator is used to replace Nearest Neighbor Interpolation to improve possible issues such as detail loss, jagged edges and image distortion. The experimental results show that compared to the original RT-DETR model, the improved algorithm has increased mAP by 3.6% and 2.1% on the SODA10M and BDD100K datasets respectively.The experiment demonstrates that FSSD-DETR significantly improves the detection accuracy while ensuring real-time performance, which has application value.