Along with the deep research on neural network, the object detection precision and speed are improved. But, computational cost is higher and higher with the deepening of network layer and increasing model volume, it cannot meet the needs that the neural network realizes fast forward reasoning directly in the embedded devices. In order to solve this problem, we study deep learning object detection optimization algorithm for embedded devices in this study. First, we choose the appropriate object detection algorithm and neural network frame structure. Then, the training and model pruning are carried out for the images collected under the specific detection scenario. Finally, the assembly instruction is optimized for the pruned object detection model transplanted to the embedded device. Compared with the original network model, the proposed model volume is reduced by 9.96% and the speed is accelerated by 8.82 times after comprehensive optimization.