The complexity and variability of traffic scenarios challenge existing human-vehicle target detection algorithms， especially when dealing with occlusion， illumination changes and multi-scale targets， existing algorithms tend to have insufficient accuracy and low computational efficiency. To solve the above problems， an improved detection model， CDC-DETR （CPPA-DWRC-CGNET-DETR）， was developed based on the RT-DETR （Real-Time DEtection TRansformer） architecture. Firstly， a Context Pre-activation Pooling Attention （CPPA） module was designed to enhance long-range dependencies and optimize feature extraction. Secondly， a Dilation-Wise Residual Connection （DWRC） module was introduced to improve multi-scale feature representation. Thirdly， a lightweight Context Guided Block （CG Block） was proposed to fuse local， surrounding， and global information and reduce computational cost. Finally， these modules were integrated to construct a high-accuracy and efficient real-time human-vehicle detection model suitable for complex traffic scenarios. Experimental results on the BDD100K dataset show that compared to RT-DETR， when the Intersection over Union （IoU） is 0.5， CDC-DETR improves the mean Average Precision （mAP） by 6.12%， increases the recall by 4.35%， and decrease the number of floating-point operations by 11.23%， enhancing computational efficiency significantly and providing an effective solution for deployment on edge devices.