基于多尺寸特征叠加的SAR舰船目标检测方法
Journal of Hunan University(Natural Sciences)（2021）
Abstract
针对SAR图像中舰船目标的检测问题,单纯基于深度学习的图像处理技术难以达到检测准确性和实时性要求.SAR图像中目标尺寸较小,且易受噪声、光斑干扰,传统方法难以提取精细特征并克服复杂条件下的背景干扰.针对以上问题,设计基于YOLOv3检测框架的端到端检测模型,借鉴了残差模块结构来避免网络退化问题.同时结合深层与浅层的不同尺寸特征图检测,使用目标基础特征提取网络参数来避免重复训练初始化过程.针对SAR图像中海上舰船成像小目标的特点改进优化了神经网络结构,实现SAR海面广域舰船目标识别分类算法,并对检测模型进行轻量化压缩处理.构建SAR图像舰船目标数据集并进行了多次目标检测识别分类实验,体现了提出的检测方法在复杂场景下有可靠的抗干扰能力和准确的目标检测识别性能.
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