基于深度学习的SAR目标检测方法

Research on SAR Target Detection Method Based on Deep Learning

高分辨率合成孔径雷达(Synthetic Aperture Radar,SAR)图像中不同目标的尺寸区别较大,这使得小目标的特征不明显,为目标检测带来了极大的挑战。针对这一问题,提出了SAR-YOLO-960算法。该算法首先改进了图像输入大小的限制,将输入图像提升到960×960像素;进而改善了YOLOv3(You Only Look Once v3)网络的整体结构,修改并添加了卷积层和残差层,整体采用64倍降采样,使其速度大大提升;最后,根据SAR图像目标的特点,改进了损失函数,从而得到了SAR-YOLO-960算法。在手工制作的高分辨率SAR图像数据集中的目标检测结果表明,相对于当前主流的检测算法,该算法性能显著提高;检测速度达32.8帧/秒,准确率达95.7%,召回率达94.5%。

The sizes of various targets in high-resolution synthetic aperture radar (SAR) images are quite different.This makes the characteristics of small targets not obvious,which brings great challenges to target detection.In response to this problem,the SAR-YOLO-960 algorithm is proposed.The algorithm first improves the image input size limit and raises the input image to 960×960 pixels.Thus the overall structure of the YOLOv3 (You Only Look Once v3) network is improved.And the convolution layer and the residual layer are modified and added.Using 64-times down sampling,the speed is greatly increased.Finally,according to the characteristics of SAR image target,the loss function is improved.As a result,the SAR-YOLO-960 algorithm is obtained.The target detection results in the hand-made high-resolution SAR image dataset show that the performance of the algorithm is significantly improved compared with the current mainstream detection algorithms;the detection speed is 32.8 frames per second,the accuracy rate is 95.7%,and the recall rate is 94.5%.