In recent years, the traditional aerial image target detection algorithms have been unable to meet the requirements of detection accuracy and speed, while the rapid development of target detection algorithms based on deep learning provides a new idea for target detection. However, this kind of algorithm is often accompanied by large scale and highly dependent on GPU devices, which makes the migration of the mobile end of the algorithm difficult. Aiming at the above problems, this paper proposes a MPSOC platform implementation scheme based on Yolo V3 algorithm. Firstly, the anchor frame of the original network is re-selected by means of k-means clustering, the detection accuracy of the algorithm is increased by adjusting the convolutional layer, and then the model scale is compressed by sensity-based pruning operation. Finally, VISDRONE data set is used to verify the Xilinx ZYNQ series MPSOC platform. The experimental results show that MAP of the improved Yolo algorithm increases by 1.3%, and the false detection rate is also greatly reduced. After the model is compressed, the detection speed is doubled and the volume becomes 37% of the original. It basically meets the design requirements of aerial image target detection, and provides a feasible solution for the implementation of deep learning algorithm in MPSOC.