Abstract: In order to solve the problems of insufficient graphics processor resources, small targets being easily missed, and slow detection speed when the deep learning method is used to detect traffic signs in high-definition panoramic images, this paper proposes a lightweight panoramic image traffic sign detection method based on deep learning. It used a small target oversampling training data generation method, image segmentation and geometric perspective detection preprocessing method, and an improved lightweight neural network Improved-Tiny-YOLOv3. Experiments were performed on the Tsinghua-Tencent 100K data set, and the mAP value reached 92.7%, and the detection speed on the Nvidia 1080Ti graphics card reached 20FPS. The experimental results show the effectiveness of the proposed method.