基于深度学习的SAR目标识别DSP设计

SAR图像目标识别主要针对桥梁、机场等战略军事目标以及飞机、坦克、汽车等战术目标，进行精确的识别分类及定位，是SAR图像解译的重要一环。首先，构建C6678的卷积神经网络主要处理层，然后结合C6678的处理及存储特性，对卷积层和网络调度进行优化设计，完成了YOLOv3-TINY目标识别网络在C6678上的设计实现方法。该方法能够对常用卷积神经网络模型进行重构及修改，解决了C6678等多核DSP处理平台运行深度学习网络的难题。实验结果表明，该方法在检测性能上与GPU一致，考虑到机载SAR的实时图像帧率，虽然该方法在C6678的实时性能相对GPU还有较大差距，但其能够满足机载SAR实时处理需求。

Deep learning-based SAR target recognition on DSP

SAR image target recognition mainly aims at strategic military targets such as bridges and airports, as well as tactical targets such as aircraft, tanks, and automobiles. Accurate identification, classification and positioning is an important part of SAR image interpretation. Firstly, the main processing layer of the convolutional neural network based on C6678 is constructed. Secondly, the processing and storage characteristics of C6678 are combined to optimize the design of the convolutional layer and network scheduling. Finally, a design and implementation method of the YOLOv3-TINY target recognition network on C6678 is completed. This method can reconstruct and modify common convolutional neural network models, and solves the problem of running deep learning networks on multi-core processing platforms such as C6678. The experimental results show that the method is consistent with GPU in detection performance. Considering the real-time image frame rate of airborne SAR, although the real-time performance of this method on C6678 is far from that of GPU, it can meet the real-time processing requirements of airborne SAR.