As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,...As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).展开更多
针对普通X86平台下硬件图形处理器不支持多层图像显示功能的情况,考虑现有雷达图像显示方法不适合实现雷达多种信息分层显示的问题,提出在Linux系统下使用内存模拟显存并结合Qt图形视图框架和OpenGL像素操作接口的方法,实现雷达信息分...针对普通X86平台下硬件图形处理器不支持多层图像显示功能的情况,考虑现有雷达图像显示方法不适合实现雷达多种信息分层显示的问题,提出在Linux系统下使用内存模拟显存并结合Qt图形视图框架和OpenGL像素操作接口的方法,实现雷达信息分层与高效图像显示。该方法能有效实现雷达显控的主要功能:雷达平面位置显示(plan position indicator,PPI)、警戒区域目标闪烁及船舶自动识别系统(automatic identification system,AIS)目标管理与显示。测试结果表明,该方法具有显示效率高、图像刷新流畅及硬件要求低等优点,该系统已成功应用于某型号船载雷达系统。展开更多
基金supported by the Shandong Provincial Natural Science Foundation,China(No.ZR2021YQ43)the National Natural Science Foundation of China(Nos.U1933135 and 61931021)the Major Science and Technology Project of Shandong Province,China(No.2019JZZY010415)。
文摘As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).
文摘针对普通X86平台下硬件图形处理器不支持多层图像显示功能的情况,考虑现有雷达图像显示方法不适合实现雷达多种信息分层显示的问题,提出在Linux系统下使用内存模拟显存并结合Qt图形视图框架和OpenGL像素操作接口的方法,实现雷达信息分层与高效图像显示。该方法能有效实现雷达显控的主要功能:雷达平面位置显示(plan position indicator,PPI)、警戒区域目标闪烁及船舶自动识别系统(automatic identification system,AIS)目标管理与显示。测试结果表明,该方法具有显示效率高、图像刷新流畅及硬件要求低等优点,该系统已成功应用于某型号船载雷达系统。
