基于CNN的SAR车辆目标检测

传统的SAR目标检测算法容易受到复杂背景的干扰，因此利用被广泛应用于图像目标检测和识别领域的Faster-RCNN方法，对复杂背景下的SAR图像进行车辆目标检测实验。在对样本数据进行预处理后对车辆真实位置进行标记，采用可视化的深度学习客户端对样本进行裁剪和旋转，扩充样本数据库。利用已充分训练的模型权重对ZF和VGG-16网络进行预训练，再利用扩充的数据集进行训练和验证，并使用包含MiniSAR数据的测试集进行测试。实验证明，ZF网络和VGG-16的检测效果类似，但是ZF网络因为网络层数更少因而检测耗时更短。

SAR Vehicle Target Detection Based on CNN

Traditional SAR target detect algorithm is easily disturbed by complex image scenes, thus Faster-CNN method, which has been widely used in image target detection and recognition field, is applied to accomplish SAR image vehicle target detection in complex background. After pre-processing the sample data, the ground truth location of the vehicle is labeled, and the visual deep-learning client is used to crop and rotate the sample to expand the sample database. The ZF and VGG-16 networks are pre-trained with well-trained model weights, then trained and verified by use of extended datasets, and finally tested with test-sets containing MiniSAR data. Experiments show that the detection effects of ZF network and VGG-16 network are similar, but ZF network takes less time because of the fewer number of network layers.