The oblique photogrammetry-based 3D modeling pipeline cannot reconstruct perfect traffic sign models， although it has been widely applied in large-scale 3D urban modeling. Therefore， a deep learning-based traffic sign embedded modeling method is proposed to fix the traffic sign 3D modeling issue using oblique photogrammetry. First， a deep neural network is used to detect the traffic signs in oblique aerial images. Second， taking advantage of the detection results， a template matching method is applied to generate the traffic sign 3D models with completed structure and perfect texture. Third， under the geometric constraints placed by the detected bounding boxes， the Scale-Invariant Feature Transform（SIFT） is used to extract the corresponding points on the traffic signs. Additionally， based on stereovision， triangulation is applied to obtain the 3D point of a single traffic sign in the city scene. Last， least-squares fitting is used to the refined point cloud to fit a plane for orientation prediction. The road signs with computer-aided design models are embedded in the 3D urban scene. The experimental results show that the proposed method achieves a high mAP in traffic sign detection and produces visually plausible embedded results， demonstrating its effectiveness for traffic sign modeling in oblique photogrammetry-based 3D scene reconstruction.