王璐;王雷欧;王东辉;

• 1:中国科学院声学研究所中科院水下航行器信息技术重点实验室
• 2:中国科学院大学

摘要(Abstract)：

对海洋资源开发的关键是实现对水下目标实时而准确的检测,但由于水介质的吸收以及悬浮粒子的散射作用,水下待测目标往往存在颜色失真、对比度低等复杂问题,这极不利于准确评估目标检测算法的性能。本文提出一种基于Faster-rcnn的水下目标检测算法,该算法以Faster-rcnn结构为主框架,将ResNet-101深度神经网络替代Faster-rcnn原本的VGG-16卷积神经网络作为特征提取和训练初始化的共享卷积网络,同时采用Water-Net网络对水下图像数据集进行增强处理,最后针对部分图像标签数据过少的问题采取了标签数据增强的方法。通过实验证明,数据集的增强性能有效提升检测算法的性能,且能满足实时检测的需求。

关键词(KeyWords)： 水下目标检测;Faster-rcnn模型;水下图像增强;Water-Net模型;数据增强

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(编号：61801469);; 中国科学院声学研究所自主部署项目(编号：ZYTS202005)

作者(Authors): 王璐;王雷欧;王东辉;

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