面向嵌入式设备的深度学习物体检测优化算法

随着深度神经网络研究地不断深入，物体检测的精度和速率都在不断提升，但是随着网络层的加深，模型体积不断增大，计算代价也越来越高，无法满足神经网络直接在嵌入式设备上实现快速前向推理的需求.为了解决这个问题，本文针对嵌入式设备进行深度学习物体检测优化算法研究.首先，选择合适的物体检测算法框架和神经网络架构；然后在此基础上针对特定检测场景下采集的图片进行训练和模型剪枝；最后，对移植到嵌入式设备上的模型剪枝后的物体检测模型进行汇编指令优化.综合优化后，与原有网络模型相比，模型体积减小9.96%，速度加快8.82倍.

Deep Learning Object Detection Optimization Algorithm for Embedded Devices

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.