## 1 Introduction  
In recent years, deep learning-based object detection algorithms have been widely used in various fields. Two-stage object detection algorithms such as the RCNN series [1, 2, 3] divide the object detection task into two consecutive stages: candidate region generation and classification and bounding box regression. Although the two-stage detection method has high computational complexity, by generating candidate regions, the number of regions that need to be processed is significantly reduced, making the second-stage classification and regression more efficient. In contrast, one-stage object detection algorithms, such as the YOLO series [4, 5, 6, 7, 8, 9], transform the object detection task into a regression problem, and complete the location and classification of the target at the same time through a single forward propagation, making its structure more concise and efficient. Recently, with the successful application of transformers in the field of computer vision, transformer-based object detection algorithms have also become a hot topic of research. DETR (end-to-end object detection with transformers), as the earliest classic algorithm that applies transformers to object detection, transforms the object detection task into a set prediction problem and directly predicts the location and category of objects from images through the self-attention mechanism. However, its training efficiency is low and the computational overhead is large. In order to make up for the shortcomings of DETR [10], RT-DETR [11] has been carefully optimized and designed on the transformer architecture, enabling it to achieve higher accuracy in real-time applications and demonstrate good performance in multiple application scenarios. In real traffic scenarios, the complexity of the driving environment brings many challenges to the research and application of automatic driving object detection. In automated driving tasks, various objects in road traffic scenes, such as vehicles, pedestrians, traffic signs, traffic signals, etc., are required to be recognized in real time and accurately, which requires efficient algorithms that can handle complex environments and a wide variety of objects. The background of pictures in real traffic scenes is complex, and the same type of objects may be in different background environments due to weather, geographic location, and angle. In this case, it is extremely important for the model to be able to capture long-distance spatial information to provide rich context for the model. Normal convolutional neural networks fuse local feature response points through convolution operators, resulting in the limited ability of models that rely only on convolution to capture the dependencies between these spatial locations. Although the receptive field can be enlarged to obtain long-range dependency information of feature maps by using large convolution kernels [12] or stacking multiple layers [13], it may bring more serious side effects, such as explosion of model parameters and computational complexity, difficulty in optimization and convergence, and network degradation [14]. ViTs [15,16] applies the standard transformer [17] in the field of natural language processing to visual images, so that each layer has a global receptive field. However, the transformer lacks some of the inductive biases inherent in CNNs and needs to learn this prior knowledge through large-scale datasets. NLNet and other works [18,19] introduced long-distance dependency information by combining non-local attention with convolutional neural networks. The non-local attention method calculates the response of a certain position as the weighted sum of the features of all positions in the input feature map, so that the receptive field of the current position can exceed the local convolution kernel to cover the entire regional feature map. However, the calculation process requires calculating the dot product similarity between the two feature response points, which will bring quadratic complexity relative to the size of the feature map.