Due to the complex computation of the deep learning network and the huge computational parameters used in the road vehicle target detection, the problem of high delay and slow processing speed exists in the target detection task on the embedded system based on ARM architecture. Aiming at the above problems, a complete embedded road vehicle target detection solution was designed and implemented in this paper. The structural re-parameterization is used in the YOLOv3-Tiny-based feature extraction network to improve the model detection accuracy, and the parallel acceleration of the convolutional neural network was deployed by Vitis-AI on the Zynq embedded platform with the DPUCZDX8G architecture acceleration core, and finally the improved YOLOv3-Tiny network model was quantified, compiled and deployed as a dynamically linked library. The experimental results show that the MAP of VOC2007 is 0.597, and the real-time processing speed is 27.7FPS. At the same time, the frame rate power consumption ratio is 1.49, which is suitable for the low power consumption requirements of edge computing devices.